diff --git a/.actor/Dockerfile b/.actor/Dockerfile new file mode 100644 index 00000000..0fcb5735 --- /dev/null +++ b/.actor/Dockerfile @@ -0,0 +1,57 @@ +# Specify the base Docker image. You can read more about +# the available images at https://docs.apify.com/sdk/js/docs/guides/docker-images +# You can also use any other image from Docker Hub. +FROM apify/actor-node:20 AS builder + +# Check preinstalled packages +RUN npm ls crawlee apify puppeteer playwright + +# Copy just package.json and package-lock.json +# to speed up the build using Docker layer cache. +COPY package*.json ./ + +# Install all dependencies. Don't audit to speed up the installation. +RUN npm install --include=dev --audit=false + +# Next, copy the source files using the user set +# in the base image. +COPY . ./ + +# Install all dependencies and build the project. +# Don't audit to speed up the installation. +RUN npm run build + +# Create final image +FROM apify/actor-node:20 + +# Check preinstalled packages +RUN npm ls crawlee apify puppeteer playwright + +# Copy just package.json and package-lock.json +# to speed up the build using Docker layer cache. +COPY package*.json ./ + +# Install NPM packages, skip optional and development dependencies to +# keep the image small. Avoid logging too much and print the dependency +# tree for debugging +RUN npm --quiet set progress=false \ + && npm install --omit=dev --omit=optional \ + && echo "Installed NPM packages:" \ + && (npm list --omit=dev --all || true) \ + && echo "Node.js version:" \ + && node --version \ + && echo "NPM version:" \ + && npm --version \ + && rm -r ~/.npm + +# Copy built JS files from builder image +COPY --from=builder /usr/src/app/dist ./dist + +# Next, copy the remaining files and directories with the source code. +# Since we do this after NPM install, quick build will be really fast +# for most source file changes. +COPY . ./ + + +# Run the image. +CMD npm run start:prod --silent diff --git a/.actor/actor.json b/.actor/actor.json new file mode 100644 index 00000000..37f81fd0 --- /dev/null +++ b/.actor/actor.json @@ -0,0 +1,9 @@ +{ + "actorSpecification": 1, + "name": "apify-mcp-server", + "title": "Model Context Protocol Server for Apify Actors", + "description": "Implementation of a Model Context Protocol (MCP) Server for Apify Actors that enables AI applications (and AI agents) to interact with Apify Actors", + "version": "0.0", + "input": "./input_schema.json", + "dockerfile": "./Dockerfile" +} diff --git a/.actor/input_schema.json b/.actor/input_schema.json new file mode 100644 index 00000000..4616796c --- /dev/null +++ b/.actor/input_schema.json @@ -0,0 +1,35 @@ +{ + "title": "Apify MCP Server", + "type": "object", + "schemaVersion": 1, + "properties": { + "actors": { + "title": "Actors to be exposed for an AI application (AI agent)", + "type": "array", + "description": "List Actors to be exposed to an AI application (AI agent) for communication via the MCP protocol. \n\n Ensure the Actor definitions fit within the LLM context by limiting the number of used Actors.", + "editor": "stringList", + "prefill": [ + "apify/instagram-scraper", + "apify/rag-web-browser", + "lukaskrivka/google-maps-with-contact-details" + ] + }, + "debugActor": { + "title": "Debug Actor", + "type": "string", + "description": "Specify the name of the Actor that will be used for debugging in normal mode", + "editor": "textfield", + "prefill": "apify/rag-web-browser", + "sectionCaption": "Debugging settings (normal mode)" + }, + "debugActorInput": { + "title": "Debug Actor input", + "type": "object", + "description": "Specify the input for the Actor that will be used for debugging in normal mode", + "editor": "json", + "prefill": { + "query": "hello world" + } + } + } +} diff --git a/.dockerignore b/.dockerignore index 3ffcc998..666793e0 100644 --- a/.dockerignore +++ b/.dockerignore @@ -16,3 +16,4 @@ node_modules data src/storage dist +.env diff --git a/.env.example b/.env.example new file mode 100644 index 00000000..1dde9d20 --- /dev/null +++ b/.env.example @@ -0,0 +1,3 @@ +APIFY_TOKEN= +# ANTHROPIC_API_KEY is only required when you want to run examples/clientStdioChat.js +ANTHROPIC_API_KEY= diff --git a/.github/scripts/before-beta-release.js b/.github/scripts/before-beta-release.js new file mode 100644 index 00000000..26ae4e1f --- /dev/null +++ b/.github/scripts/before-beta-release.js @@ -0,0 +1,32 @@ +const { execSync } = require('child_process'); +const fs = require('fs'); +const path = require('path'); + +const PKG_JSON_PATH = path.join(__dirname, '..', '..', 'package.json'); + +const pkgJson = require(PKG_JSON_PATH); // eslint-disable-line import/no-dynamic-require + +const PACKAGE_NAME = pkgJson.name; +const VERSION = pkgJson.version; + +const nextVersion = getNextVersion(VERSION); +console.log(`before-deploy: Setting version to ${nextVersion}`); // eslint-disable-line no-console +pkgJson.version = nextVersion; + +fs.writeFileSync(PKG_JSON_PATH, `${JSON.stringify(pkgJson, null, 2)}\n`); + +function getNextVersion(version) { + const versionString = execSync(`npm show ${PACKAGE_NAME} versions --json`, { encoding: 'utf8' }); + const versions = JSON.parse(versionString); + + if (versions.some((v) => v === VERSION)) { + console.error(`before-deploy: A release with version ${VERSION} already exists. Please increment version accordingly.`); // eslint-disable-line no-console + process.exit(1); + } + + const prereleaseNumbers = versions + .filter((v) => (v.startsWith(VERSION) && v.includes('-'))) + .map((v) => Number(v.match(/\.(\d+)$/)[1])); + const lastPrereleaseNumber = Math.max(-1, ...prereleaseNumbers); + return `${version}-beta.${lastPrereleaseNumber + 1}`; +} diff --git a/.github/workflows/check.yaml b/.github/workflows/check.yaml new file mode 100644 index 00000000..6742f943 --- /dev/null +++ b/.github/workflows/check.yaml @@ -0,0 +1,30 @@ +# This workflow runs for every pull request to lint and test the proposed changes. + +name: Check + +on: + pull_request: + + # Push to master will trigger code checks + push: + branches: + - master + tags-ignore: + - "**" # Ignore all tags to prevent duplicate builds when tags are pushed. + +jobs: + lint: + name: Lint + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + - name: Use Node.js 22 + uses: actions/setup-node@v4 + with: + node-version: 22 + cache: 'npm' + cache-dependency-path: 'package-lock.json' + - name: Install Dependencies + run: npm ci + - run: npm run lint diff --git a/.github/workflows/pre_release.yaml b/.github/workflows/pre_release.yaml new file mode 100644 index 00000000..5ea800e2 --- /dev/null +++ b/.github/workflows/pre_release.yaml @@ -0,0 +1,103 @@ +name: Create a pre-release + +on: + # Push to master will deploy a beta version + push: + branches: + - master + tags-ignore: + - "**" # Ignore all tags to prevent duplicate builds when tags are pushed. + +concurrency: + group: release + cancel-in-progress: false + +jobs: + release_metadata: + if: "!startsWith(github.event.head_commit.message, 'docs') && !startsWith(github.event.head_commit.message, 'ci') && startsWith(github.repository, 'apify/')" + name: Prepare release metadata + runs-on: ubuntu-latest + outputs: + version_number: ${{ steps.release_metadata.outputs.version_number }} + changelog: ${{ steps.release_metadata.outputs.changelog }} + steps: + - uses: apify/workflows/git-cliff-release@main + name: Prepare release metadata + id: release_metadata + with: + release_type: prerelease + existing_changelog_path: CHANGELOG.md + + wait_for_checks: + name: Wait for code checks to pass + runs-on: ubuntu-latest + steps: + - uses: lewagon/wait-on-check-action@v1.3.4 + with: + ref: ${{ github.ref }} + repo-token: ${{ secrets.GITHUB_TOKEN }} + check-name: 'Lint' + wait-interval: 5 + + update_changelog: + needs: [ release_metadata ] + name: Update changelog + runs-on: ubuntu-latest + outputs: + changelog_commitish: ${{ steps.commit.outputs.commit_long_sha || github.sha }} + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + with: + token: ${{ secrets.APIFY_SERVICE_ACCOUNT_GITHUB_TOKEN }} + + - name: Use Node.js 22 + uses: actions/setup-node@v4 + with: + node-version: 22 + + - name: Update package version in package.json + run: npm version --no-git-tag-version --allow-same-version ${{ needs.release_metadata.outputs.version_number }} + + - name: Update CHANGELOG.md + uses: DamianReeves/write-file-action@master + with: + path: CHANGELOG.md + write-mode: overwrite + contents: ${{ needs.release_metadata.outputs.changelog }} + + - name: Commit changes + id: commit + uses: EndBug/add-and-commit@v9 + with: + author_name: Apify Release Bot + author_email: noreply@apify.com + message: "chore(release): Update changelog and package version [skip ci]" + + publish_to_npm: + name: Publish to NPM + needs: [ release_metadata ] + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + ref: ${{ needs.update_changelog.changelog_commitish }} + - name: Use Node.js 22 + uses: actions/setup-node@v4 + with: + node-version: 22 + - name: Install dependencies + run: | + echo "access=public" >> .npmrc + echo "//registry.npmjs.org/:_authToken=${NPM_TOKEN}" >> .npmrc + npm install + - # Check version consistency and increment pre-release version number for beta only. + name: Bump pre-release version + run: node ./.github/scripts/before-beta-release.js + - name: Publish to NPM + run: npm publish --tag beta + +env: + NODE_AUTH_TOKEN: ${{ secrets.APIFY_SERVICE_ACCOUNT_NPM_TOKEN }} + NPM_TOKEN: ${{ secrets.APIFY_SERVICE_ACCOUNT_NPM_TOKEN }} diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml new file mode 100644 index 00000000..43ef27ed --- /dev/null +++ b/.github/workflows/release.yaml @@ -0,0 +1,119 @@ +name: Create a release + +on: + # Trigger a stable version release via GitHub's UI, with the ability to specify the type of release. + workflow_dispatch: + inputs: + release_type: + description: Release type + required: true + type: choice + default: auto + options: + - auto + - custom + - patch + - minor + - major + custom_version: + description: The custom version to bump to (only for "custom" type) + required: false + type: string + default: "" + +concurrency: + group: release + cancel-in-progress: false + +jobs: + release_metadata: + name: Prepare release metadata + runs-on: ubuntu-latest + outputs: + version_number: ${{ steps.release_metadata.outputs.version_number }} + tag_name: ${{ steps.release_metadata.outputs.tag_name }} + changelog: ${{ steps.release_metadata.outputs.changelog }} + release_notes: ${{ steps.release_metadata.outputs.release_notes }} + steps: + - uses: apify/workflows/git-cliff-release@main + name: Prepare release metadata + id: release_metadata + with: + release_type: ${{ inputs.release_type }} + custom_version: ${{ inputs.custom_version }} + existing_changelog_path: CHANGELOG.md + + update_changelog: + needs: [ release_metadata ] + name: Update changelog + runs-on: ubuntu-latest + outputs: + changelog_commitish: ${{ steps.commit.outputs.commit_long_sha || github.sha }} + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + with: + token: ${{ secrets.APIFY_SERVICE_ACCOUNT_GITHUB_TOKEN }} + + - name: Use Node.js 22 + uses: actions/setup-node@v4 + with: + node-version: 22 + + - name: Update package version in package.json + run: npm version --no-git-tag-version --allow-same-version ${{ needs.release_metadata.outputs.version_number }} + + - name: Update CHANGELOG.md + uses: DamianReeves/write-file-action@master + with: + path: CHANGELOG.md + write-mode: overwrite + contents: ${{ needs.release_metadata.outputs.changelog }} + + - name: Commit changes + id: commit + uses: EndBug/add-and-commit@v9 + with: + author_name: Apify Release Bot + author_email: noreply@apify.com + message: "chore(release): Update changelog and package version [skip ci]" + + create_github_release: + name: Create github release + needs: [release_metadata, update_changelog] + runs-on: ubuntu-latest + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + steps: + - name: Create release + uses: softprops/action-gh-release@v2 + with: + tag_name: ${{ needs.release_metadata.outputs.tag_name }} + name: ${{ needs.release_metadata.outputs.version_number }} + target_commitish: ${{ needs.update_changelog.outputs.changelog_commitish }} + body: ${{ needs.release_metadata.outputs.release_notes }} + + publish_to_npm: + name: Publish to NPM + needs: [ update_changelog ] + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + ref: ${{ needs.update_changelog.changelog_commitish }} + - name: Use Node.js 22 + uses: actions/setup-node@v4 + with: + node-version: 22 + - name: Install dependencies + run: | + echo "access=public" >> .npmrc + echo "//registry.npmjs.org/:_authToken=${NPM_TOKEN}" >> .npmrc + npm install + - name: Publish to NPM + run: npm publish --tag latest + +env: + NODE_AUTH_TOKEN: ${{ secrets.APIFY_SERVICE_ACCOUNT_NPM_TOKEN }} + NPM_TOKEN: ${{ secrets.APIFY_SERVICE_ACCOUNT_NPM_TOKEN }} diff --git a/.gitignore b/.gitignore index 191865ad..1e23c8f4 100644 --- a/.gitignore +++ b/.gitignore @@ -17,3 +17,4 @@ storage/key_value_stores/default/* # Added by Apify CLI .venv +.env diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 00000000..123e7fed --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,6 @@ +This changelog summarizes all changes of the Apify Model Context Protocol Server + +### 0.0.1 (2025-01-xx) + +🚀 Features +- Add functionality to call Apify Actors via Model Context Protocol diff --git a/README.md b/README.md index 3f31891d..47615eaa 100644 --- a/README.md +++ b/README.md @@ -1,80 +1,316 @@ -## Scrape single-page in TypeScript template +# Apify Model Context Protocol (MCP) Server -A template for scraping data from a single web page in TypeScript (Node.js). The URL of the web page is passed in via input, which is defined by the [input schema](https://docs.apify.com/platform/actors/development/input-schema). The template uses the [Axios client](https://axios-http.com/docs/intro) to get the HTML of the page and the [Cheerio library](https://cheerio.js.org/) to parse the data from it. The data are then stored in a [dataset](https://docs.apify.com/sdk/js/docs/guides/result-storage#dataset) where you can easily access them. +Implementation of an MCP server for all [Apify Actors](https://apify.com/store). +This server enables interaction with one or more Apify Actors that can be defined in the MCP server configuration. -The scraped data in this template are page headings but you can easily edit the code to scrape whatever you want from the page. +The server can be used in two ways: +- 🇦 **Apify MCP Server Actor**: runs an HTTP server supporting the MCP protocol via Server-Sent Events. +- ⾕ **Apify MCP Server Stdio**: provides support for the MCP protocol via standard input/output stdio. -## Included features +# 🎯 What does Apify MCP server do? -- **[Apify SDK](https://docs.apify.com/sdk/js/)** - a toolkit for building [Actors](https://apify.com/actors) -- **[Input schema](https://docs.apify.com/platform/actors/development/input-schema)** - define and easily validate a schema for your Actor's input -- **[Dataset](https://docs.apify.com/sdk/js/docs/guides/result-storage#dataset)** - store structured data where each object stored has the same attributes -- **[Axios client](https://axios-http.com/docs/intro)** - promise-based HTTP Client for Node.js and the browser -- **[Cheerio](https://cheerio.js.org/)** - library for parsing and manipulating HTML and XML +The MCP Server Actor allows an AI assistant to use any [Apify Actor](https://apify.com/store) as a tool to perform a specific task. +For example it can: +- use [Facebook Posts Scraper](https://apify.com/apify/facebook-posts-scraper) to extract data from Facebook posts from multiple pages/profiles +- use [Google Maps Email Extractor](https://apify.com/lukaskrivka/google-maps-with-contact-details) to extract Google Maps contact details +- use [Google Search Results Scraper](https://apify.com/apify/google-search-scraper) to scrape Google Search Engine Results Pages (SERPs) +- use [Instagram Scraper](https://apify.com/apify/instagram-scraper) to scrape Instagram posts, profiles, places, hashtags, photos, and comments +- use [RAG Web Browser](https://apify.com/apify/web-scraper) to perform a web search, scrape the top N URLs from the results, and return content -## How it works +To interact with the Apify MCP server, you can use MCP clients such as [Claude Desktop](https://claude.ai/download), [Superinference.ai](https://superinterface.ai/), or [LibreChat](https://www.librechat.ai/). +Additionally, you can use simple example clients found in the [examples](https://github.com/apify/actor-mcp-server/tree/main/src/examples) directory. -1. `Actor.getInput()` gets the input where the page URL is defined -2. `axios.get(url)` fetches the page -3. `cheerio.load(response.data)` loads the page data and enables parsing the headings -4. This parses the headings from the page and here you can edit the code to parse whatever you need from the page +When you have Actors integrated with the MCP server, you can ask: +- "Search web and summarize recent trends about AI Agents" +- "Find top 10 best Italian restaurants in San Francisco" +- "Find and analyze Instagram profile of The Rock" +- "Provide a step-by-step guide on using the Model Context Protocol with source URLs." +- "What Apify Actors I can use?" - ```javascript - $("h1, h2, h3, h4, h5, h6").each((_i, element) => {...}); - ``` +In the future, we plan to load Actors dynamically and provide Apify's dataset and key-value store as resources. +See the [Roadmap](#-roadmap-january-2025) for more details. -5. `Actor.pushData(headings)` stores the headings in the dataset +# 🔄 What is the Model Context Protocol? -## Resources +The Model Context Protocol (MCP) allows AI applications (and AI agents), such as Claude Desktop, to connect to external tools and data sources. +MCP is an open protocol that enables secure, controlled interactions between AI applications, AI Agents, and local or remote resources. -- [Web scraping in Node.js with Axios and Cheerio](https://blog.apify.com/web-scraping-with-axios-and-cheerio/) -- [Web scraping with Cheerio in 2023](https://blog.apify.com/web-scraping-with-cheerio/) -- [Video tutorial](https://www.youtube.com/watch?v=yTRHomGg9uQ) on building a scraper using CheerioCrawler -- [Written tutorial](https://docs.apify.com/academy/web-scraping-for-beginners/challenge) on building a scraper using CheerioCrawler -- [Integration with Zapier](https://apify.com/integrations), Make, Google Drive, and others -- [Video guide on getting scraped data using Apify API](https://www.youtube.com/watch?v=ViYYDHSBAKM) -- A short guide on how to build web scrapers using code templates: +# 🧱 Components -[web scraper template](https://www.youtube.com/watch?v=u-i-Korzf8w) +## Tools -## Getting started +Any [Apify Actor](https://apify.com/store) can be used as a tool. +By default, the server is pre-configured with the Actors specified below, but it can be overridden by providing Actor input. -For complete information [see this article](https://docs.apify.com/platform/actors/development#build-actor-locally). To run the actor use the following command: +```text +'apify/instagram-scraper' +'apify/rag-web-browser' +'lukaskrivka/google-maps-with-contact-details' +``` +The MCP server loads the Actor input schema and creates MCP tools corresponding to the Actors. +See this example of input schema for the [RAG Web Browser](https://apify.com/apify/rag-web-browser/input-schema). + +The tool name must always be the full Actor name, such as `apify/rag-web-browser`. +The arguments for an MCP tool represent the input parameters of the Actor. +For example, for the `apify/rag-web-browser` Actor, the arguments are: + +```json +{ + "query": "restaurants in San Francisco", + "maxResults": 3 +} +``` +You don't need to specify the input parameters or which Actor to call, everything is managed by an LLM. +When a tool is called, the arguments are automatically passed to the Actor by the LLM. +You can refer to the specific Actor's documentation for a list of available arguments. + +## Prompt & Resources + +The server does not provide any resources and prompts. +We plan to add Apify's dataset and key-value store as resources in the future. + +# ⚙️ Usage + +The Apify MCP Server can be used in two ways: **as an Apify Actor** running at Apify platform +or as a **local server** running on your machine. + +## 🇦 MCP Server Actor + +### Standby web server + +The Actor runs in [**Standby mode**](https://docs.apify.com/platform/actors/running/standby) with an HTTP web server that receives and processes requests. + +Start server with default Actors. To use the Apify MCP Server with set of default Actors, +send an HTTP GET request with your [Apify API token](https://console.apify.com/settings/integrations) to the following URL. +``` +https://actors-mcp-server.apify.actor?token= +``` +It is also possible to start the MCP server with a different set of Actors. +To do this, create a [task](https://docs.apify.com/platform/actors/running/tasks) and specify the list of Actors you want to use. + +Then, run task in Standby mode with the selected Actors using your Apify API token. +```shell +https://actors-mcp-server-task.apify.actor?token= +``` + +You can find a list of all available Actors in the [Apify Store](https://apify.com/store). + +#### 💬 Interact with the MCP Server + +Once the server is running, you can interact with Server-Sent Events (SSE) to send messages to the server and receive responses. +You can use MCP clients such as [Superinference.ai](https://superinterface.ai/) or [LibreChat](https://www.librechat.ai/). +([Claude Desktop](https://claude.ai/download) does not support SSE transport yet) + +In the client settings you need to provide server configuration: +```json +{ + "mcpServers": { + "apify": { + "type": "sse", + "url": "https://actors-mcp-server.apify.actor/sse", + "env": { + "APIFY_TOKEN": "your-apify-token" + } + } + } +} +``` +Alternatively, you can use simple python [client_see.py](https://github.com/apify/actor-mcp-server/tree/main/src/examples/client_sse.py) or test the server using `curl` commands. + +1. Initiate Server-Sent-Events (SSE) by sending a GET request to the following URL: + ``` + curl https://actors-mcp-server.apify.actor/sse?token= + ``` + The server will respond with a `sessionId`, which you can use to send messages to the server: + ```shell + event: endpoint + data: /message?sessionId=a1b + ``` + +2. Send a message to the server by making a POST request with the `sessionId`: + ```shell + curl -X POST "https://actors-mcp-server.apify.actor?token=&session_id=a1b" -H "Content-Type: application/json" -d '{ + "jsonrpc": "2.0", + "id": 1, + "method": "tools/call", + "params": { + "arguments": { "searchStringsArray": ["restaurants in San Francisco"], "maxCrawledPlacesPerSearch": 3 }, + "name": "lukaskrivka/google-maps-with-contact-details" + } + }' + ``` + The MCP server will start the Actor `lukaskrivka/google-maps-with-contact-details` with the provided arguments as input parameters. + For this POST request, the server will respond with: + + ```text + Accepted + ``` + +3. Receive the response. The server will invoke the specified Actor as a tool using the provided query parameters and stream the response back to the client via SSE. + The response will be returned as JSON text. + + ```text + event: message + data: {"result":{"content":[{"type":"text","text":"{\"searchString\":\"restaurants in San Francisco\",\"rank\":1,\"title\":\"Gary Danko\",\"description\":\"Renowned chef Gary Danko's fixed-price menus of American cuisine ... \",\"price\":\"$100+\"...}}]}} + ``` + +## ⾕ MCP Server at a local host + +### Prerequisites + +- MacOS or Windows +- The latest version of Claude Desktop must be installed (or another MCP client) +- [Node.js](https://nodejs.org/en) (v18 or higher) +- [Apify API Token](https://docs.apify.com/platform/integrations/api#api-token) (`APIFY_TOKEN`) + +### Install + +Follow the steps below to set up and run the server on your local machine: +First, clone the repository using the following command: ```bash -apify run +git clone git@github.com:apify/actor-mcp-server.git ``` +Navigate to the project directory and install the required dependencies: -## Deploy to Apify +```bash +cd actor-mcp-server +npm install +``` + +Before running the server, you need to build the project: + +```bash +npm run build +``` + +#### Claude Desktop + +Configure Claude Desktop to recognize the MCP server. + +1. Open your Claude Desktop configuration and edit the following file: + + - On macOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json` + - On Windows: `%APPDATA%/Claude/claude_desktop_config.json` + + ```text + "mcpServers": { + "apify": { + "command": "npx", + "args": [ + "/path/to/actor-mcp-server/dist/index.js" + ] + "env": { + "APIFY_TOKEN": "your-apify-token" + } + } + } + ``` + Alternatively, you can use the following command to select one or more Apify Actors: + ```text + "mcpServers": { + "apify-mcp-server": { + "command": "npx", + "args": [ + "/path/to/actor-mcp-server/dist/index.js", + "--actors", + "lukaskrivka/google-maps-with-contact-details,apify/instagram-scraper" + ] + "env": { + "APIFY_TOKEN": "your-apify-token" + } + } + } + ``` + +2. Restart Claude Desktop + + - Fully quit Claude Desktop (ensure it’s not just minimized or closed). + - Restart Claude Desktop. + - Look for the 🔌 icon to confirm that the Exa server is connected. + +3. Examples + + You can ask Claude to perform web searches, such as: + ```text + Find and analyze recent research papers about LLMs. + Find top 10 best Italian restaurants in San Francisco. + Find and analyze instagram profile of the Rock. + ``` -### Connect Git repository to Apify +#### Stdio clients -If you've created a Git repository for the project, you can easily connect to Apify: +Create environment file `.env` with the following content: +```text +APIFY_TOKEN=your-apify-token +# ANTHROPIC_API_KEY is only required when you want to run examples/clientStdioChat.js +ANTHROPIC_API_KEY=your-anthropic-api-token +``` +In the `examples` directory, you can find two clients that interact with the server via +standard input/output (stdio): +1. [`clientStdio.ts`](https://github.com/apify/actor-mcp-server/tree/main/src/examples/clientStdio.ts) + This client script starts the MCP server with two specified Actors. + It then calls the `apify/rag-web-browser` tool with a query and prints the result. + It demonstrates how to connect to the MCP server, list available tools, and call a specific tool using stdio transport. + ```bash + node dist/examples/clientStdio.js + ``` + +2. [`clientStdioChat.ts`](https://github.com/apify/actor-mcp-server/tree/main/src/examples/clientStdioChat.ts) + This client script also starts the MCP server but provides an interactive command-line chat interface. + It prompts the user to interact with the server, allowing for dynamic tool calls and responses. + This example is useful for testing and debugging interactions with the MCP server in conversational manner. + + ```bash + node dist/examples/clientStdioChat.js + ``` + +# 👷🏼 Development -1. Go to [Actor creation page](https://console.apify.com/actors/new) -2. Click on **Link Git Repository** button +## Prerequisites -### Push project on your local machine to Apify +- [Node.js](https://nodejs.org/en) (v18 or higher) +- Python 3.6 or higher -You can also deploy the project on your local machine to Apify without the need for the Git repository. +Create environment file `.env` with the following content: +```text +APIFY_TOKEN=your-apify-token +# ANTHROPIC_API_KEY is only required when you want to run examples/clientStdioChat.js +ANTHROPIC_API_KEY=your-anthropic-api-token +``` +## Local client (SSE) + +To test the server with the SSE transport, you can use python script `examples/client_sse.py`: +Currently, the node.js client does not support to establish a connection to remote server witch custom headers. +You need to change URL to your local server URL in the script. -1. Log in to Apify. You will need to provide your [Apify API Token](https://console.apify.com/account/integrations) to complete this action. +```bash +python src/examples/client_sse.py +``` - ```bash - apify login - ``` +## Debugging -2. Deploy your Actor. This command will deploy and build the Actor on the Apify Platform. You can find your newly created Actor under [Actors -> My Actors](https://console.apify.com/actors?tab=my). +Since MCP servers operate over standard input/output (stdio), debugging can be challenging. +For the best debugging experience, use the [MCP Inspector](https://github.com/modelcontextprotocol/inspector). - ```bash - apify push - ``` +Build the actor-mcp-server package: + +```bash +npm run build +``` + +You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command: + +```bash +npx @modelcontextprotocol/inspector node /path/to/actor-mcp-server/dist/index.js --env APIFY_TOKEN=your-apify-token +``` -## Documentation reference +Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging. -To learn more about Apify and Actors, take a look at the following resources: +# 🚀 Roadmap (January 2025) -- [Apify SDK for JavaScript documentation](https://docs.apify.com/sdk/js) -- [Apify SDK for Python documentation](https://docs.apify.com/sdk/python) -- [Apify Platform documentation](https://docs.apify.com/platform) -- [Join our developer community on Discord](https://discord.com/invite/jyEM2PRvMU) +- Document examples for [Superinference.ai](https://superinterface.ai/) and [LibreChat](https://www.librechat.ai/). +- Provide tools to search for Actors and load them as needed. +- Add Apify's dataset and key-value store as resources. +- Add tools such as Actor logs and Actor runs for debugging. +- Prune Actors input schema to reduce context size. diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 00000000..65006803 --- /dev/null +++ b/package-lock.json @@ -0,0 +1,6644 @@ +{ + "name": "@apify/mcp-server", + "version": "0.1.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "@apify/mcp-server", + "version": "0.1.0", + "license": "MIT", + "dependencies": { + "@modelcontextprotocol/sdk": "^1.1.1", + "ajv": 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"0.1.0", + "type": "module", + "description": "Model Context Protocol Server for Apify Actors", + "engines": { + "node": ">=18.0.0" + }, + "main": "dist/index.js", + "bin": { + "apify-mcp-server": "dist/index.js" + }, + "repository": { + "type": "git", + "url": "https://github.com/apify/actor-mcp-server.git" + }, + "bugs": { + "url": "https://github.com/apify/actor-mcp-server/issues" + }, + "homepage": "https://apify.com/apify/mcp-server", + "keywords": [ + "apify", + "mcp", + "server", + "actors", + "model context protocol" + ], + "dependencies": { + "@modelcontextprotocol/sdk": "^1.1.1", + "ajv": "^8.17.1", + "apify": "^3.2.6", + "apify-client": "^2.11.1", + "express": "^4.21.2", + "minimist": "^1.2.8" + }, + "devDependencies": { + "@anthropic-ai/sdk": "^0.33.1", + "@anthropic-ai/tokenizer": "^0.0.4", + "@apify/eslint-config": "^0.5.0-beta.2", + "@apify/tsconfig": "^0.1.0", + "@types/express": "^4.0.0", + "@types/minimist": "^1.2.5", + "dotenv": "^16.4.7", + "eslint": "^9.17.0", + "eventsource": "^3.0.2", + "tsx": "^4.6.2", + "typescript": "^5.3.3", + "typescript-eslint": "^8.18.2" + }, + "scripts": { + "start": "npm run start:dev", + "start:prod": "node dist/main.js", + "start:dev": "tsx src/main.ts", + "lint": "eslint .", + "lint:fix": "eslint . --fix", + "build": "tsc", + "test": "echo \"Error: oops, the actor has no tests yet, sad!\" && exit 1", + "watch": "tsc --watch" + }, + "author": "Apify", + "license": "MIT" +} diff --git a/src/actorDefinition.ts b/src/actorDefinition.ts new file mode 100644 index 00000000..42b147b0 --- /dev/null +++ b/src/actorDefinition.ts @@ -0,0 +1,87 @@ +import { Ajv } from 'ajv'; +import { ApifyClient } from 'apify-client'; + +import { log } from './logger.js'; +import type { ActorDefinitionWithDesc, Tool } from './types'; + +/** + * Get actor input schema by actor name. + * First, fetch the actor details to get the default build tag and buildId. + * Then, fetch the build details and return actorName, description, and input schema. + * @param {string} actorFullName - The full name of the actor. + * @returns {Promise} - The actor definition with description or null if not found. + */ +async function fetchActorDefinition(actorFullName: string): Promise { + if (!process.env.APIFY_TOKEN) { + log.error('APIFY_TOKEN is required but not set. Please set it as an environment variable'); + return null; + } + const client = new ApifyClient({ token: process.env.APIFY_TOKEN }); + const actorClient = client.actor(actorFullName); + + try { + // Fetch actor details + const actor = await actorClient.get(); + if (!actor) { + log.error(`Failed to fetch input schema for actor: ${actorFullName}. Actor not found.`); + return null; + } + + // fnesveda: The default build is not necessarily tagged, you can specify any build number as default build. + // There will be a new API endpoint to fetch a default build. + // For now, we'll use the tagged build, it will work for 90% of Actors. Later, we can update this. + const tag = actor.defaultRunOptions?.build || ''; + const buildId = actor.taggedBuilds?.[tag]?.buildId || ''; + + if (!buildId) { + log.error(`Failed to fetch input schema for actor: ${actorFullName}. Build ID not found.`); + return null; + } + // Fetch build details and return the input schema + const buildDetails = await client.build(buildId).get(); + if (buildDetails?.actorDefinition) { + const actorDefinitions = buildDetails?.actorDefinition as ActorDefinitionWithDesc; + actorDefinitions.description = actor.description || ''; + actorDefinitions.name = actorFullName; + return actorDefinitions; + } + return null; + } catch (error) { + log.error(`Failed to fetch input schema for actor: ${actorFullName} with error ${error}.`); + return null; + } +} + +/** + * Fetches actor input schemas by actor full names and creates MCP tools. + * + * This function retrieves the input schemas for the specified actors and compiles them into MCP tools. + * It uses the AJV library to validate the input schemas. + * + * Tool name can't contain /, so it is replaced with _ + * + * @param {string[]} actors - An array of actor full names. + * @returns {Promise} - A promise that resolves to an array of MCP tools. + */ +export async function getActorsAsTools(actors: string[]): Promise { + // Fetch input schemas in parallel + const ajv = new Ajv({ coerceTypes: 'array', strict: false }); + const results = await Promise.all(actors.map(fetchActorDefinition)); + const tools = []; + for (const result of results) { + if (result) { + try { + tools.push({ + name: result.name.replace('/', '_'), + actorName: result.name, + description: result.description, + inputSchema: result.input || {}, + ajvValidate: ajv.compile(result.input || {}), + }); + } catch (validationError) { + log.error(`Failed to compile AJV schema for actor: ${result.name}. Error: ${validationError}`); + } + } + } + return tools; +} diff --git a/src/const.ts b/src/const.ts new file mode 100644 index 00000000..cdbae1d1 --- /dev/null +++ b/src/const.ts @@ -0,0 +1,20 @@ +export const SERVER_NAME = 'apify-mcp-server'; +export const SERVER_VERSION = '0.1.0'; + +export const defaults = { + actors: [ + 'apify/instagram-scraper', + 'apify/rag-web-browser', + 'lukaskrivka/google-maps-with-contact-details', + ], +}; + +export const ACTOR_OUTPUT_MAX_CHARS_PER_ITEM = 2_000; +export const ACTOR_OUTPUT_TRUNCATED_MESSAGE = `Output was truncated because it will not fit into context.` + + ` There is no reason to call this tool again!`; + +export enum Routes { + ROOT = '/', + SSE = '/sse', + MESSAGE = '/message', +} diff --git a/src/examples/clientSse.ts b/src/examples/clientSse.ts new file mode 100644 index 00000000..cac69710 --- /dev/null +++ b/src/examples/clientSse.ts @@ -0,0 +1,100 @@ +/* eslint-disable no-console */ +/** + * Connect to the MCP server using SSE transport and call a tool. + * The Actors MCP Server will load default Actors. + * + * !!! This example needs to be fixed as it does not work !!! + */ + +import path from 'path'; +import { fileURLToPath } from 'url'; + +import { Client } from '@modelcontextprotocol/sdk/client/index.js'; +import { SSEClientTransport } from '@modelcontextprotocol/sdk/client/sse.js'; +import { CallToolResultSchema } from '@modelcontextprotocol/sdk/types.js'; +import dotenv from 'dotenv'; +import { EventSource } from 'eventsource'; + +// Resolve dirname equivalent in ES module +const filename = fileURLToPath(import.meta.url); +const dirname = path.dirname(filename); + +dotenv.config({ path: path.resolve(dirname, '../../.env') }); + +const SERVER_URL = 'https://actors-mcp-server/sse'; +// We need to change forward slash / to underscore _ in the tool name as Anthropic does not allow forward slashes in the tool name +const SELECTED_TOOL = 'apify_rag-web-browser'; + +if (!process.env.APIFY_TOKEN) { + console.error('APIFY_TOKEN is required but not set in the environment variables.'); + process.exit(1); +} + +if (typeof globalThis.EventSource === 'undefined') { + globalThis.EventSource = EventSource as unknown as typeof globalThis.EventSource; +} + +async function main(): Promise { + const transport = new SSEClientTransport( + new URL(SERVER_URL), + { + requestInit: { + headers: { + authorization: `Bearer ${process.env.APIFY_TOKEN}`, + }, + }, + eventSourceInit: { + // The EventSource package augments EventSourceInit with a "fetch" parameter. + // You can use this to set additional headers on the outgoing request. + // Based on this example: https://github.com/modelcontextprotocol/typescript-sdk/issues/118 + async fetch(input: Request | URL | string, init?: RequestInit) { + const headers = new Headers(init?.headers || {}); + headers.set('authorization', `Bearer ${process.env.APIFY_TOKEN}`); + return fetch(input, { ...init, headers }); + }, + // We have to cast to "any" to use it, since it's non-standard + } as any, // eslint-disable-line @typescript-eslint/no-explicit-any + }, + ); + const client = new Client( + { name: 'example-client', version: '1.0.0' }, + { capabilities: {} }, + ); + + try { + // Connect to the MCP server + await client.connect(transport); + + // List available tools + const tools = await client.listTools(); + console.log('Available tools:', tools); + + if (tools.tools.length === 0) { + console.log('No tools available'); + return; + } + + const selectedTool = tools.tools.find((tool) => tool.name === SELECTED_TOOL); + if (!selectedTool) { + console.error(`The specified tool: ${selectedTool} is not available. Exiting.`); + return; + } + + // Call a tool + console.log('Calling actor ...'); + const result = await client.callTool( + { name: SELECTED_TOOL, arguments: { query: 'web browser for Anthropic' } }, + CallToolResultSchema, + ); + console.log('Tool result:', JSON.stringify(result, null, 2)); + } catch (error: unknown) { + if (error instanceof Error) { + console.error('Error:', error.message); + console.error(error.stack); + } else { + console.error('An unknown error occurred:', error); + } + } +} + +await main(); diff --git a/src/examples/clientStdio.ts b/src/examples/clientStdio.ts new file mode 100644 index 00000000..e9c9c336 --- /dev/null +++ b/src/examples/clientStdio.ts @@ -0,0 +1,85 @@ +/* eslint-disable no-console */ +/** + * Connect to the MCP server using stdio transport and call a tool. + * This script uses a selected tool without LLM involvement. + * You need to provide the path to the MCP server and `APIFY_TOKEN` in the `.env` file. + * You can choose actors to run in the server, for example: `apify/rag-web-browser`. + */ + +import { execSync } from 'child_process'; +import path from 'path'; +import { fileURLToPath } from 'url'; + +import { Client } from '@modelcontextprotocol/sdk/client/index.js'; +import { StdioClientTransport } from '@modelcontextprotocol/sdk/client/stdio.js'; +import { CallToolResultSchema } from '@modelcontextprotocol/sdk/types.js'; +import dotenv from 'dotenv'; + +// Resolve dirname equivalent in ES module +const filename = fileURLToPath(import.meta.url); +const dirname = path.dirname(filename); + +dotenv.config({ path: path.resolve(dirname, '../../.env') }); +const SERVER_PATH = path.resolve(dirname, '../../dist/index.js'); +const NODE_PATH = execSync(process.platform === 'win32' ? 'where node' : 'which node').toString().trim(); + +const TOOLS = 'apify/rag-web-browser,lukaskrivka/google-maps-with-contact-details'; +const SELECTED_TOOL = 'apify_rag-web-browser'; // We need to change / to _ in the tool name + +if (!process.env.APIFY_TOKEN) { + console.error('APIFY_TOKEN is required but not set in the environment variables.'); + process.exit(1); +} + +// Create server parameters for stdio connection +const transport = new StdioClientTransport({ + command: NODE_PATH, + args: [SERVER_PATH, '--actors', TOOLS], + env: { APIFY_TOKEN: process.env.APIFY_TOKEN || '' }, +}); + +// Create a new client instance +const client = new Client( + { name: 'example-client', version: '0.1.0' }, + { capabilities: {} }, +); + +// Main function to run the example client +async function run() { + try { + // Connect to the MCP server + await client.connect(transport); + + // List available tools + const tools = await client.listTools(); + console.log('Available tools:', tools); + + if (tools.tools.length === 0) { + console.log('No tools available'); + return; + } + + // Example: Call the first available tool + const selectedTool = tools.tools.find((tool) => tool.name === SELECTED_TOOL); + + if (!selectedTool) { + console.error(`The specified tool: ${selectedTool} is not available. Exiting.`); + return; + } + + // Call a tool + console.log('Calling actor ...'); + const result = await client.callTool( + { name: SELECTED_TOOL, arguments: { query: 'web browser for Anthropic' } }, + CallToolResultSchema, + ); + console.log('Tool result:', JSON.stringify(result)); + } catch (error) { + console.error('Error:', error); + } +} + +run().catch((error) => { + console.error('Unhandled error:', error); + process.exit(1); +}); diff --git a/src/examples/clientStdioChat.ts b/src/examples/clientStdioChat.ts new file mode 100644 index 00000000..1ebdf607 --- /dev/null +++ b/src/examples/clientStdioChat.ts @@ -0,0 +1,227 @@ +/* eslint-disable no-console */ +/** + * Create a simple chat client that connects to the Model Context Protocol server using the stdio transport. + * Based on the user input, the client sends a query to the MCP server, retrieves results and processes them. + * + * You can expect the following output: + * + * MCP Client Started! + * Type your queries or 'quit|q|exit' to exit. + * You: Find to articles about AI agent and return URLs + * [internal] Received response from Claude: [{"type":"text","text":"I'll search for information about AI agents + * and provide you with a summary."},{"type":"tool_use","id":"tool_01He9TkzQfh2979bbeuxWVqM","name":"search", + * "input":{"query":"what are AI agents definition capabilities applications","maxResults":2}}] + * [internal] Calling tool: {"name":"search","arguments":{"query":"what are AI agents definition ... + * I can help analyze the provided content about AI agents. + * This appears to be crawled content from AWS and IBM websites explaining what AI agents are. + * Let me summarize the key points: + */ + +import { execSync } from 'child_process'; +import path from 'path'; +import * as readline from 'readline'; +import { fileURLToPath } from 'url'; + +import { Anthropic } from '@anthropic-ai/sdk'; +import type { Message, ToolUseBlock, MessageParam } from '@anthropic-ai/sdk/resources/messages'; +import { Client } from '@modelcontextprotocol/sdk/client/index.js'; +import { StdioClientTransport } from '@modelcontextprotocol/sdk/client/stdio.js'; +import { CallToolResultSchema } from '@modelcontextprotocol/sdk/types.js'; +import dotenv from 'dotenv'; + +const filename = fileURLToPath(import.meta.url); +const dirname = path.dirname(filename); + +dotenv.config({ path: path.resolve(dirname, '../../.env') }); + +const REQUEST_TIMEOUT = 120_000; // 2 minutes +const MAX_TOKENS = 2048; // Maximum tokens for Claude response + +// const CLAUDE_MODEL = 'claude-3-5-sonnet-20241022'; // the most intelligent model +// const CLAUDE_MODEL = 'claude-3-5-haiku-20241022'; // a fastest model +const CLAUDE_MODEL = 'claude-3-haiku-20240307'; // a fastest and most compact model for near-instant responsiveness +const DEBUG = true; +const DEBUG_SERVER_PATH = path.resolve(dirname, '../../dist/index.js'); + +const NODE_PATH = execSync('which node').toString().trim(); + +dotenv.config(); // Load environment variables from .env + +export type Tool = { + name: string; + description: string | undefined; + input_schema: unknown; +} + +class MCPClient { + private anthropic: Anthropic; + private client = new Client( + { + name: 'example-client', + version: '0.1.0', + }, + { + capabilities: {}, // Optional capabilities + }, + ); + + private tools: Tool[] = []; + + constructor() { + this.anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY }); + } + + /** + * Start the server using node and provided server script path. + * Connect to the server using stdio transport and list available tools. + */ + async connectToServer(serverArgs: string[]) { + const transport = new StdioClientTransport({ + command: NODE_PATH, + args: serverArgs, + env: { APIFY_TOKEN: process.env.APIFY_TOKEN || '' }, + }); + + await this.client.connect(transport); + const response = await this.client.listTools(); + + this.tools = response.tools.map((x) => ({ + name: x.name, + description: x.description, + input_schema: x.inputSchema, + })); + console.log('Connected to server with tools:', this.tools.map((x) => x.name)); + } + + /** + * Process LLM response and check whether it contains any tool calls. + * If a tool call is found, call the tool and return the response and save the results to messages with type: user. + * If the tools response is too large, truncate it to the limit. + */ + async processMsg(response: Message, messages: MessageParam[]): Promise { + for (const content of response.content) { + if (content.type === 'text') { + messages.push({ role: 'assistant', content: content.text }); + } else if (content.type === 'tool_use') { + await this.handleToolCall(content, messages); + } + } + return messages; + } + + /** + * Call the tool and return the response. + */ + private async handleToolCall(content: ToolUseBlock, messages: MessageParam[], toolCallCount = 0): Promise { + // eslint-disable-next-line @typescript-eslint/no-explicit-any + const params = { name: content.name, arguments: content.input as any }; + console.log(`[internal] Calling tool (count: ${toolCallCount}): ${JSON.stringify(params)}`); + let results; + try { + results = await this.client.callTool(params, CallToolResultSchema, { timeout: REQUEST_TIMEOUT }); + if (results.content instanceof Array && results.content.length !== 0) { + const text = results.content.map((x) => x.text); + messages.push({ role: 'user', content: `Tool result: ${text.join('\n\n')}` }); + } else { + messages.push({ role: 'user', content: `No results retrieved from ${params.name}` }); + } + } catch (error) { + messages.push({ role: 'user', content: `Error calling tool: ${params.name}, error: ${error}` }); + } + // Get next response from Claude + const nextResponse: Message = await this.anthropic.messages.create({ + model: CLAUDE_MODEL, + max_tokens: MAX_TOKENS, + messages, + tools: this.tools as any[], // eslint-disable-line @typescript-eslint/no-explicit-any + }); + + for (const c of nextResponse.content) { + if (c.type === 'text') { + messages.push({ role: 'assistant', content: c.text }); + } else if (c.type === 'tool_use' && toolCallCount < 3) { + return await this.handleToolCall(c, messages, toolCallCount + 1); + } + } + + return messages; + } + + /** + * Process user query by sending it to the server and returning the response. + * Also, process any tool calls. + */ + async processQuery(query: string, messages: MessageParam[]): Promise { + messages.push({ role: 'user', content: query }); + const response: Message = await this.anthropic.messages.create({ + model: CLAUDE_MODEL, + max_tokens: MAX_TOKENS, + messages, + tools: this.tools as any[], // eslint-disable-line @typescript-eslint/no-explicit-any + }); + console.log('[internal] Received response from Claude:', JSON.stringify(response.content)); + return await this.processMsg(response, messages); + } + + /** + * Create a chat loop that reads user input from the console and sends it to the server for processing. + */ + async chatLoop() { + const rl = readline.createInterface({ + input: process.stdin, + output: process.stdout, + prompt: 'You: ', + }); + + console.log("MCP Client Started!\nType your queries or 'quit|q|exit' to exit."); + rl.prompt(); + + let lastPrintMessage = 0; + const messages: MessageParam[] = []; + rl.on('line', async (input) => { + const v = input.trim().toLowerCase(); + if (v === 'quit' || v === 'q' || v === 'exit') { + rl.close(); + return; + } + try { + await this.processQuery(input, messages); + for (let i = lastPrintMessage + 1; i < messages.length; i++) { + if (messages[i].role === 'assistant') { + console.log('CLAUDE:', messages[i].content); + } else if (messages[i].role === 'user') { + console.log('USER:', messages[i].content.slice(0, 500), '...'); + } else { + console.log('CLAUDE[thinking]:', messages[i].content); + } + } + lastPrintMessage += messages.length; + } catch (error) { + console.error('Error processing query:', error); + } + rl.prompt(); + }); + } +} + +async function main() { + const client = new MCPClient(); + + if (process.argv.length < 3) { + if (DEBUG) { + process.argv.push(DEBUG_SERVER_PATH); + } else { + console.error('Usage: node '); + process.exit(1); + } + } + + try { + await client.connectToServer(process.argv.slice(2)); + await client.chatLoop(); + } catch (error) { + console.error('Error:', error); + } +} + +main().catch(console.error); diff --git a/src/examples/client_sse.py b/src/examples/client_sse.py new file mode 100644 index 00000000..76c3c539 --- /dev/null +++ b/src/examples/client_sse.py @@ -0,0 +1,48 @@ +""" +Test Apify MCP Server using SSE client + +It is using python client as the typescript one does not support custom headers when connecting to the SSE server. + +Install python dependencies (assumes you have python installed): +> pip install requests python-dotenv mcp +""" + +import asyncio +import os +from pathlib import Path + +import requests +from dotenv import load_dotenv +from mcp.client.session import ClientSession +from mcp.client.sse import sse_client + +load_dotenv(Path(__file__).resolve().parent.parent.parent / ".env") + +MCP_SERVER_URL = "https://actors-mcp-server.apify.actor" + +HEADERS = {"Authorization": f"Bearer {os.getenv('APIFY_TOKEN')}"} + +async def run() -> None: + + print("Start MCP Server with Actors") + r = requests.get(MCP_SERVER_URL, headers=HEADERS) + print("MCP Server Response:", r.json(), end="\n\n") + + async with sse_client(url=f"{MCP_SERVER_URL}/sse", timeout=60, headers=HEADERS) as (read, write): + async with ClientSession(read, write) as session: + await session.initialize() + + tools = await session.list_tools() + print("Available Tools:", tools, end="\n\n") + + if hasattr(tools, "tools") and not tools.tools: + print("No tools available!") + return + + result = await session.call_tool("apify/rag-web-browser", { "query": "example.com", "maxResults": 3 }) + print("Tools Call Result:") + + for content in result.content: + print(content) + +asyncio.run(run()) diff --git a/src/index.ts b/src/index.ts new file mode 100644 index 00000000..328833c8 --- /dev/null +++ b/src/index.ts @@ -0,0 +1,34 @@ +/** + * This script initializes and starts the Apify MCP server using the Stdio transport. + * + * Usage: + * node --actors= + * + * Command-line arguments: + * --actors - A comma-separated list of actor full names to add to the server. + * + * Example: + * node index.js --actors=apify/google-search-scraper,apify/instagram-scraper + */ + +import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js'; +import minimist from 'minimist'; + +import { ApifyMcpServer } from './server.js'; + +const argv = minimist(process.argv.slice(2)); +const argActors = argv.actors?.split(',').map((actor: string) => actor.trim()) || []; + +async function main() { + const server = new ApifyMcpServer(); + await (argActors.length !== 0 + ? server.addToolsFromActors(argActors) + : server.addToolsFromDefaultActors()); + const transport = new StdioServerTransport(); + await server.connect(transport); +} + +main().catch((error) => { + console.error('Server error:', error); // eslint-disable-line no-console + process.exit(1); +}); diff --git a/src/input.ts b/src/input.ts new file mode 100644 index 00000000..c90bbe7a --- /dev/null +++ b/src/input.ts @@ -0,0 +1,19 @@ +import type { Input } from './types.js'; + +/** + * Process input parameters, split actors string into an array + * @param originalInput + * @returns input + */ +export async function processInput(originalInput: Partial): Promise { + const input = originalInput as Input; + + // actors can be a string or an array of strings + if (input.actors && typeof input.actors === 'string') { + input.actors = input.actors.split(',').map((format: string) => format.trim()) as string[]; + } + if (!input.actors || input.actors.length === 0) { + throw new Error('The `actors` parameter must be a non-empty array.'); + } + return input; +} diff --git a/src/logger.ts b/src/logger.ts new file mode 100644 index 00000000..39ab7c96 --- /dev/null +++ b/src/logger.ts @@ -0,0 +1,5 @@ +import { log } from 'apify'; + +log.setLevel(log.LEVELS.DEBUG); + +export { log }; diff --git a/src/main.ts b/src/main.ts new file mode 100644 index 00000000..db1bccd6 --- /dev/null +++ b/src/main.ts @@ -0,0 +1,106 @@ +import type { ParsedUrlQuery } from 'querystring'; +import { parse } from 'querystring'; + +import { SSEServerTransport } from '@modelcontextprotocol/sdk/server/sse.js'; +import { Actor } from 'apify'; +import type { Request, Response } from 'express'; +import express from 'express'; + +import { Routes } from './const.js'; +import { processInput } from './input.js'; +import { log } from './logger.js'; +import { ApifyMcpServer } from './server.js'; +import type { Input } from './types.js'; + +await Actor.init(); + +const STANDBY_MODE = Actor.getEnv().metaOrigin === 'STANDBY'; +const HOST = Actor.isAtHome() ? process.env.ACTOR_STANDBY_URL : 'http://localhost'; +const PORT = Actor.isAtHome() ? process.env.ACTOR_STANDBY_PORT : 3001; + +const app = express(); + +const mcpServer = new ApifyMcpServer(); +let transport: SSEServerTransport; + +const HELP_MESSAGE = `Connect to the server with GET request to ${HOST}/sse?token=YOUR-APIFY-TOKEN` + + ` and then send POST requests to ${HOST}/message?token=YOUR-APIFY-TOKEN.`; + +/** + * Process input parameters and update tools + * If URL contains query parameter actors, add tools from actors, otherwise add tools from default actors + * @param url + */ +async function processParamsAndUpdateTools(url: string) { + const params = parse(url.split('?')[1] || '') as ParsedUrlQuery; + delete params.token; + log.debug(`Received input parameters: ${JSON.stringify(params)}`); + const input = await processInput(params as Input); + if (input.actors) { + await mcpServer.addToolsFromActors(input.actors as string[]); + } else { + log.debug(`Server is running in STANDBY mode with the following Actors (tools): ${mcpServer.getToolNames()}. + To use different Actors, provide them in query parameter "actors" or include them in the Actor Task input.`); + } +} + +app.get(Routes.ROOT, async (req: Request, res: Response) => { + try { + log.info(`Received GET message at: ${req.url}`); + await processParamsAndUpdateTools(req.url); + res.status(200).json({ message: `Actor is using Model Context Protocol. ${HELP_MESSAGE}` }).end(); + } catch (error) { + log.error(`Error in GET ${Routes.ROOT} ${error}`); + res.status(500).json({ message: 'Internal Server Error' }).end(); + } +}); + +app.head(Routes.ROOT, (_req: Request, res: Response) => { + res.status(200).end(); +}); + +app.get(Routes.SSE, async (req: Request, res: Response) => { + try { + log.info(`Received GET message at: ${req.url}`); + await processParamsAndUpdateTools(req.url); + transport = new SSEServerTransport(Routes.MESSAGE, res); + await mcpServer.connect(transport); + } catch (error) { + log.error(`Error in GET ${Routes.SSE}: ${error}`); + res.status(500).json({ message: 'Internal Server Error' }).end(); + } +}); + +app.post(Routes.MESSAGE, async (req: Request, res: Response) => { + try { + log.info(`Received POST message at: ${req.url}`); + await transport.handlePostMessage(req, res); + } catch (error) { + log.error(`Error in POST ${Routes.MESSAGE}: ${error}`); + res.status(500).json({ message: 'Internal Server Error' }).end(); + } +}); + +// Catch-all for undefined routes +app.use((req: Request, res: Response) => { + res.status(404).json({ message: `There is nothing at route ${req.method} ${req.originalUrl}. ${HELP_MESSAGE}` }).end(); +}); + +const input = await processInput((await Actor.getInput>()) ?? ({} as Input)); +log.info(`Loaded input: ${JSON.stringify(input)} `); + +if (STANDBY_MODE) { + log.info('Actor is running in the STANDBY mode.'); + await mcpServer.addToolsFromDefaultActors(); + app.listen(PORT, () => { + log.info(`The Actor web server is listening for user requests at ${HOST}.`); + }); +} else { + log.info('Actor is not designed to run in the NORMAL model (use this mode only for debugging purposes)'); + + if (input && !input.debugActor && !input.debugActorInput) { + await Actor.fail('If you need to debug a specific actor, please provide the debugActor and debugActorInput fields in the input.'); + } + await mcpServer.callActorGetDataset(input.debugActor!, input.debugActorInput!); + await Actor.exit(); +} diff --git a/src/server.ts b/src/server.ts new file mode 100644 index 00000000..1e54fdf3 --- /dev/null +++ b/src/server.ts @@ -0,0 +1,153 @@ +#!/usr/bin/env node +/** + * Model Context Protocol (MCP) server for Apify Actors + */ +import { Server } from '@modelcontextprotocol/sdk/server/index.js'; +import type { Transport } from '@modelcontextprotocol/sdk/shared/transport.js'; +import { CallToolRequestSchema, ListToolsRequestSchema } from '@modelcontextprotocol/sdk/types.js'; +import { Actor } from 'apify'; +import { ApifyClient } from 'apify-client'; + +import { getActorsAsTools } from './actorDefinition.js'; +import { + ACTOR_OUTPUT_MAX_CHARS_PER_ITEM, + ACTOR_OUTPUT_TRUNCATED_MESSAGE, + defaults, + SERVER_NAME, + SERVER_VERSION, +} from './const.js'; +import { log } from './logger.js'; +import type { Tool } from './types'; + +/** + * Create Apify MCP server + */ +export class ApifyMcpServer { + private server: Server; + private tools: Map; + + constructor() { + this.server = new Server( + { + name: SERVER_NAME, + version: SERVER_VERSION, + }, + { + capabilities: { + tools: {}, + }, + }, + ); + this.tools = new Map(); + this.setupErrorHandling(); + this.setupToolHandlers(); + } + + /** + * Calls an Apify actor and retrieves the dataset items. + * + * It requires the `APIFY_TOKEN` environment variable to be set. + * If the `APIFY_IS_AT_HOME` the dataset items are pushed to the Apify dataset. + * + * @param {string} actorName - The name of the actor to call. + * @param {unknown} input - The input to pass to the actor. + * @returns {Promise} - A promise that resolves to an array of dataset items. + * @throws {Error} - Throws an error if the `APIFY_TOKEN` is not set + */ + public async callActorGetDataset(actorName: string, input: unknown): Promise { + if (!process.env.APIFY_TOKEN) { + throw new Error('APIFY_TOKEN is required but not set. Please set it as an environment variable'); + } + try { + log.info(`Calling actor ${actorName} with input: ${JSON.stringify(input)}`); + const client = new ApifyClient({ token: process.env.APIFY_TOKEN }); + const actorClient = client.actor(actorName); + + const results = await actorClient.call(input); + const dataset = await client.dataset(results.defaultDatasetId).listItems(); + log.info(`Actor ${actorName} finished with ${dataset.items.length} items`); + + if (process.env.APIFY_IS_AT_HOME) { + await Actor.pushData(dataset.items); + log.info(`Pushed ${dataset.items.length} items to the dataset`); + } + return dataset.items; + } catch (error) { + log.error(`Error calling actor: ${error}. Actor: ${actorName}, input: ${JSON.stringify(input)}`); + throw new Error(`Error calling actor: ${error}`); + } + } + + public async addToolsFromActors(actors: string[]) { + const tools = await getActorsAsTools(actors); + this.updateTools(tools); + } + + public async addToolsFromDefaultActors() { + await this.addToolsFromActors(defaults.actors); + } + + public updateTools(tools: Tool[]): void { + for (const tool of tools) { + this.tools.set(tool.name, tool); + log.info(`Added/Updated tool: ${tool.name}`); + } + } + + public getToolNames(): string[] { + return Array.from(this.tools.keys()); + } + + private setupErrorHandling(): void { + this.server.onerror = (error) => { + console.error('[MCP Error]', error); // eslint-disable-line no-console + }; + process.on('SIGINT', async () => { + await this.server.close(); + process.exit(0); + }); + } + + private setupToolHandlers(): void { + this.server.setRequestHandler(ListToolsRequestSchema, async () => { + return { tools: Array.from(this.tools.values()) }; + }); + + /** + * Handles the request to call a tool. + * @param {object} request - The request object containing tool name and arguments. + * @throws {Error} - Throws an error if the tool is unknown or arguments are invalid. + */ + this.server.setRequestHandler(CallToolRequestSchema, async (request) => { + const { name, arguments: args } = request.params; + + // Anthropic can't handle '/' in tool names. The replace is only necessary when calling the tool from stdio clients. + const tool = this.tools.get(name) || this.tools.get(name.replace('/', '_')); + if (!tool) { + throw new Error(`Unknown tool: ${name}`); + } + log.info(`Validate arguments for tool: ${tool.name} with arguments: ${JSON.stringify(args)}`); + if (!tool.ajvValidate(args)) { + throw new Error(`Invalid arguments for tool ${tool.name}: args: ${JSON.stringify(args)} error: ${JSON.stringify(tool?.ajvValidate.errors)}`); + } + + try { + const items = await this.callActorGetDataset(tool.actorName, args); + const content = items.map((item) => { + const text = JSON.stringify(item).slice(0, ACTOR_OUTPUT_MAX_CHARS_PER_ITEM); + return text.length === ACTOR_OUTPUT_MAX_CHARS_PER_ITEM + ? { type: 'text', text: `${text} ... ${ACTOR_OUTPUT_TRUNCATED_MESSAGE}` } + : { type: 'text', text }; + }); + return { content }; + } catch (error) { + log.error(`Error calling tool: ${error}`); + throw new Error(`Error calling tool: ${error}`); + } + }); + } + + async connect(transport: Transport): Promise { + await this.server.connect(transport); + } +} diff --git a/src/types.ts b/src/types.ts new file mode 100644 index 00000000..717d740f --- /dev/null +++ b/src/types.ts @@ -0,0 +1,20 @@ +import type { ValidateFunction } from 'ajv'; +import type { ActorDefinition } from 'apify-client'; + +export type Input = { + actors: string[] | string; + debugActor?: string; + debugActorInput?: unknown; +}; + +export interface ActorDefinitionWithDesc extends ActorDefinition { + description: string; +} + +export interface Tool { + name: string; + actorName: string; + description: string; + inputSchema: object; + ajvValidate: ValidateFunction; +} diff --git a/tsconfig.eslint.json b/tsconfig.eslint.json index a9d3394d..398b6a50 100644 --- a/tsconfig.eslint.json +++ b/tsconfig.eslint.json @@ -1,4 +1,4 @@ { "extends": "./tsconfig.json", - "include": ["./src/**/*", "./test/**/*", "./scripts/**/*", "./types/**/*"] + "include": ["./src/**/*"] } diff --git a/tsconfig.json b/tsconfig.json index 833ac36d..fb02aa71 100644 --- a/tsconfig.json +++ b/tsconfig.json @@ -11,6 +11,6 @@ "typeRoots": ["./types", "./node_modules/@types"], "strict": true }, - "include": ["./src/**/*", "./types/**/*"], + "include": ["./src/**/*"], "exclude": ["node_modules"] }