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docs/clients.mdx

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@@ -58,9 +58,9 @@ This page provides an overview of applications that support the Model Context Pr
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| [TheiaAI/TheiaIDE][TheiaAI/TheiaIDE] ||||||| Supports tools for Agents in Theia AI and the AI-powered Theia IDE |
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| [Tome][Tome] ||||||| Supports tools, manages MCP servers. |
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| [TypingMind App][TypingMind App] ||||||| Supports tools at app-level (appear as plugins) or when assigned to Agents |
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| [VS Code GitHub Copilot][VS Code] | | ||| || Supports dynamic tool/roots discovery, secure secret configuration, and explicit tool prompting |
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| [VS Code GitHub Copilot][VS Code] | | ||| || Supports dynamic tool/roots discovery, secure secret configuration, and explicit tool prompting |
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| [Warp][Warp] ||||||| Supports tools, resources, and most of the discovery criteria |
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| [WhatsMPC][WhatsMPC] ||||||| Supports tools for Remote MCP Servers in WhatsApp |
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| [WhatsMCP][WhatsMCP] ||||||| Supports tools for Remote MCP Servers in WhatsApp |
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| [Windsurf Editor][Windsurf] ||||||| Supports tools with AI Flow for collaborative development. |
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| [Witsy][Witsy] ||||||| Supports tools in Witsy. |
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| [Zed][Zed] ||||||| Prompts appear as slash commands |
@@ -115,7 +115,7 @@ This page provides an overview of applications that support the Model Context Pr
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[Windsurf]: https://codeium.com/windsurf
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[gptme]: https://github.com/gptme/gptme
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[Warp]: https://www.warp.dev/
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[WhatsMPC]: https://wassist.app/mcp/
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[WhatsMCP]: https://wassist.app/mcp/
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[Witsy]: https://github.com/nbonamy/witsy
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[Zed]: https://zed.dev
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[Zencoder]: https://zencoder.ai
@@ -590,7 +590,7 @@ Programmatically assemble prompts for LLMs using [GenAIScript](https://microsoft
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- Full support of all major MCP features (tools, prompts, resources, and subscriptions)
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- Fast, seamless UI for debugging MCP capabilities
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- MCP config integration (Claude, VSCode, etc.) for fast first-time experience in testing MCPs
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- Integration with history, varibles, and collections for re-use and collaboration
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- Integration with history, variables, and collections for reuse and collaboration
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### Slack MCP Client
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- **Live tool/resource discovery**: view tools and resources from each running MCP server
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- **Configurable startup**: set MCP servers to start automatically with Warp or launch them manually as needed
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### WhatsMPC
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### WhatsMCP
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[WhatsMPC](https://wassist.app/mcp/) is an MCP client for WhatsApp. WhatsMCP lets you interact with your AI stack from the comfort of a WhatsApp chat.
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[WhatsMCP](https://wassist.app/mcp/) is an MCP client for WhatsApp. WhatsMCP lets you interact with your AI stack from the comfort of a WhatsApp chat.
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**Key features:**
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### Witsy
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[Witsy](https://github.com/nbonamy/witsy) is an AI desktop assistant, supoorting Anthropic models and MCP servers as LLM tools.
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[Witsy](https://github.com/nbonamy/witsy) is an AI desktop assistant, supporting Anthropic models and MCP servers as LLM tools.
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**Key features:**
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docs/docs.json

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]
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}
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]
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},
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{
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"tab": "SDKs",
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"icon": "book-open",
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"groups": [
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{
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"group": "Java",
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"pages": [
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"sdk/java/mcp-overview",
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"sdk/java/mcp-client",
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"sdk/java/mcp-server"
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]
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}
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]
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},
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},
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{
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"tab": "Specification",
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"icon": "book",

docs/quickstart/client.mdx

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@@ -3,7 +3,7 @@ title: "For Client Developers"
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description: "Get started building your own client that can integrate with all MCP servers."
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---
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In this tutorial, you'll learn how to build a LLM-powered chatbot client that connects to MCP servers. It helps to have gone through the [Server quickstart](/quickstart/server) that guides you through the basic of building your first server.
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In this tutorial, you'll learn how to build an LLM-powered chatbot client that connects to MCP servers. It helps to have gone through the [Server quickstart](/quickstart/server) that guides you through the basics of building your first server.
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<Tabs>
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<Tab title="Python">
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command,
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args: [serverScriptPath],
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});
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this.mcp.connect(this.transport);
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await this.mcp.connect(this.transport);
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const toolsResult = await this.mcp.listTools();
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this.tools = toolsResult.tools.map((tool) => {
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});
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const finalText = [];
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const toolResults = [];
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for (const content of response.content) {
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if (content.type === "text") {
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name: toolName,
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arguments: toolArgs,
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});
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toolResults.push(result);
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finalText.push(
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`[Calling tool ${toolName} with args ${JSON.stringify(toolArgs)}]`
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);
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dotnet add package ModelContextProtocol --prerelease
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dotnet add package Anthropic.SDK
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dotnet add package Microsoft.Extensions.Hosting
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dotnet add package Microsoft.Extensions.AI
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```
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## Setting up your API key

docs/quickstart/server.mdx

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@@ -1085,7 +1085,7 @@ Create a new boot starter application using the `spring-ai-starter-mcp-client` d
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```
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and set the `spring.ai.mcp.client.stdio.servers-configuration` property to point to your `claude_desktop_config.json`.
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You can re-use the existing Anthropic Desktop configuration:
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You can reuse the existing Anthropic Desktop configuration:
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```properties
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spring.ai.mcp.client.stdio.servers-configuration=file:PATH/TO/claude_desktop_config.json
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<Note>
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When creating the `ApplicationHostBuilder`, ensure you use `CreateEmptyApplicationBuilder` instead of `CreateDefaultBuilder`. This ensures that the server does not write any additional messages to the console. This is only neccessary for servers using STDIO transport.
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When creating the `ApplicationHostBuilder`, ensure you use `CreateEmptyApplicationBuilder` instead of `CreateDefaultBuilder`. This ensures that the server does not write any additional messages to the console. This is only necessary for servers using STDIO transport.
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</Note>
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docs/sdk/java/mcp-server.mdx

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</Tabs>
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The `McpSchema.CompletionReference` definition defines the type (`PromptRefernce` or `ResourceRefernce`) and the identifier for the completion specification (e.g handler).
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The handler function processes requests and returns the complition response.
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The handler function processes requests and returns the completion response.
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The first argument is `McpAsyncServerExchange` for client interaction, and the second argument is a `CompleteRequest` instance.
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Check the [using completion](/sdk/java/mcp-client#using-completion) to learn how to use the completion capabilities on the client side.

docs/specification/draft/basic/authorization.mdx

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Authorization servers **MUST** validate exact redirect URIs against pre-registered values to prevent redirection attacks.
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MCP clients **SHOULD** use and verify state parameters in the authorization code flow
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and discard any results that do not include or have a mis-match with the original state.
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and discard any results that do not include or have a mismatch with the original state.
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Authorization servers **MUST** take precautions to prevent redirecting user agents to untrusted URI's, following suggestions laid out in [OAuth 2.1 Section 7.12.2](https://datatracker.ietf.org/doc/html/draft-ietf-oauth-v2-1-12#section-7.12.2)
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docs/specification/draft/basic/lifecycle.mdx

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},
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"clientInfo": {
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"name": "ExampleClient",
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"title": "Example Client Display Name",
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"version": "1.0.0"
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}
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}
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},
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"serverInfo": {
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"name": "ExampleServer",
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"title": "Example Server Display Name",
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"version": "1.0.0"
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},
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"instructions": "Optional instructions for the client"

docs/specification/draft/server/prompts.mdx

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"prompts": [
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{
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"name": "code_review",
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"title": "Request Code Review",
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"description": "Asks the LLM to analyze code quality and suggest improvements",
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"arguments": [
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{
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A prompt definition includes:
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- `name`: Unique identifier for the prompt
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- `title`: Optional human-readable name of the prompt for display purposes.
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- `description`: Optional human-readable description
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- `arguments`: Optional list of arguments for customization
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"type": "resource",
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"resource": {
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"uri": "resource://example",
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"name": "example",
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"title": "My Example Resource",
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"mimeType": "text/plain",
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"text": "Resource content"
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}

docs/specification/draft/server/resources.mdx

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{
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"uri": "file:///project/src/main.rs",
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"name": "main.rs",
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"title": "Rust Software Application Main File",
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"description": "Primary application entry point",
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"mimeType": "text/x-rust"
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}
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"contents": [
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{
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"uri": "file:///project/src/main.rs",
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"name": "main.rs",
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"title": "Rust Software Application Main File",
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"mimeType": "text/x-rust",
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"text": "fn main() {\n println!(\"Hello world!\");\n}"
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}
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{
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"uriTemplate": "file:///{path}",
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"name": "Project Files",
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"title": "📁 Project Files",
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"description": "Access files in the project directory",
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"mimeType": "application/octet-stream"
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}
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"jsonrpc": "2.0",
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"method": "notifications/resources/updated",
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"params": {
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"uri": "file:///project/src/main.rs"
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"uri": "file:///project/src/main.rs",
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"title": "Rust Software Application Main File"
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}
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}
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```
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A resource definition includes:
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- `uri`: Unique identifier for the resource
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- `name`: Human-readable name
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- `name`: The name of the resource.
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- `title`: Optional human-readable name of the resource for display purposes.
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- `description`: Optional description
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- `mimeType`: Optional MIME type
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- `size`: Optional size in bytes
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```json
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"uri": "file:///example.txt",
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"name": "example.txt",
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"title": "Example Text File",
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"mimeType": "text/plain",
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"text": "Resource content"
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}
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```json
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"name": "example.png",
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"title": "Example Image",
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"mimeType": "image/png",
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"blob": "base64-encoded-data"
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docs/specification/draft/server/tools.mdx

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"tools": [
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"name": "get_weather",
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"title": "Weather Information Provider",
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"description": "Get current weather information for a location",
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"inputSchema": {
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"type": "object",
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A tool definition includes:
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- `name`: Unique identifier for the tool
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- `title`: Optional human-readable name of the tool for display purposes.
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- `description`: Human-readable description of functionality
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- `inputSchema`: JSON Schema defining expected parameters
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- `outputSchema`: Optional JSON Schema defining expected output structure
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"type": "resource",
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"resource": {
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"title": "Project Rust Main File",
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"mimeType": "text/x-rust",
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"text": "fn main() {\n println!(\"Hello world!\");\n}"
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}
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```json
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{
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"name": "get_weather_data",
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"title": "Weather Data Retriever",
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"description": "Get current weather data for a location",
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"inputSchema": {
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"type": "object",

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