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Copy file name to clipboardExpand all lines: sources/platform/integrations/ai/mcp.md
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@@ -16,12 +16,12 @@ The _Apify Model Context Protocol (MCP) Server_ allows AI applications to connec
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You can use the Apify MCP Server in two ways:
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-_HTTPS Endpoint_`mcp.apify.com`: Connect your MCP client through OAuth or by including `Authorization: Bearer <APIFY_TOKEN>` header in your requests.
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-`https://mcp.apify.com` for streamable transport
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-`https://mcp.apify.com/sse` for legacy SSE transport
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-_Standard Input/Output (stdio)_: Ideal for local integrations and command-line tools such as the Claude for Desktop client.
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- Set MCP client server command to `npx @apify/actors-mcp-server` and environment variable `APIFY_TOKEN` to your Apify API token
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- See `npx @apify/actors-mcp-server --help` for more options
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-_HTTPS Endpoint_`mcp.apify.com`: Connect your MCP client by including `Authorization: Bearer <APIFY_TOKEN>` header in your requests.
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-`https://mcp.apify.com` for streamable transport
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-`https://mcp.apify.com/sse` for legacy SSE transport
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You could also use legacy option by running [Apify Actors MCP Server](https://apify.com/apify/actors-mcp-server) as an Actor.
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1._Apify API Token:_ Get your personal API token from the **Integrations** section in [Apify Console](https://console.apify.com/account#/integrations). This token will be used to authorize the MCP server to run Actors on your behalf.
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1._MCP client:_ An AI agent or client that supports MCP. This could be Anthropic Claude for Desktop, a VS Code extension with MCP support, Apify’s web-based Tester MCP Client, or any custom client implementation. See supported MCP clients in [official documentation](https://modelcontextprotocol.io/clients).
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## Example usage (Streamable HTTP with OAuth)
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We recommend connecting through OAuth for a secure and simple authentication process.
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During setup, provide the server URL `https://mcp.apify.com`. You will then be redirected to your browser to sign in to your Apify account and approve the connection. The configuration steps may vary slightly depending on your MCP client.
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```json
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{
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"mcpServers": {
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"apify": {
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"url": "https://mcp.apify.com"
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}
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}
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}
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```
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## Example usage (local stdio with Claude for Desktop)
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Let’s walk through an example of using Claude for Desktop with the Apify MCP Server:
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_Tools for adding and removing Actors are enabled by default._
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You can disable these tools by setting the parameter `?enableAddingActors=false` in the MCP Server URL, or with the CLI flag `--enable-adding-actors=false` (can also be set in Claude for Desktop config args as `--enable-adding-actors=false`).
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Not all MCP client frameworks allow dynamic tool addition at runtime, but Apify’s own tester client does, if adding Actors is enabled.
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-_Via config file:_ When using Claude for Desktop, you can specify which Actors should be immediately available by configuring your `mcpServers` settings. Add the Actors as a comma-separated list in the `--actors` parameter, as shown in the example below. This pre-loads your selected tools without requiring discovery during conversations, ideal for workflows with predictable tool needs.
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-_Via url:_ If you are using Streamable HTTP or SSE protocol, you could add `actors` query parameter with Actor names separated by comma:
-_Via config file:_ For local stdio connection, you can specify which Actors should be immediately available by configuring your json configuration. Add the Actors as a comma-separated list in the `--actors` parameter, as shown in the example below. This pre-loads your selected tools without requiring discovery during conversations, ideal for workflows with predictable tool needs.
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```json
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{
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}
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```
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In summary, you can start with a broad set (everything open and discoverable) or a narrow set (just what you need) and even expand tools on the fly, giving your agent a lot of flexibility without overwhelming it initially.
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