{"name":"n8n-nodes-mcp: Seamless AI Integration with Model Context Protocol","description":"The n8n-nodes-mcp is a custom n8n node designed to facilitate interaction with Model Context Protocol (MCP) servers. It empowers n8n workflows to connect with AI models, access resources, execute tools, and utilize prompts in a standardized manner, significantly enhancing AI agent capabilities.","github":"https://github.com/nerding-io/n8n-nodes-mcp","url":"https://osrepos.com/repo/nerding-io-n8n-nodes-mcp","source":"osrepos.com","sourceDescription":"This repository profile is provided by osrepos.com, an open source repository discovery platform.","repositoryProfile":"https://osrepos.com/repo/nerding-io-n8n-nodes-mcp","generatedFor":"open source discovery and AI-assisted research","markdown":"https://osrepos.com/repo/nerding-io-n8n-nodes-mcp.md","json":"https://osrepos.com/repo/nerding-io-n8n-nodes-mcp.json","topics":["TypeScript","n8n","AI","Automation","Model Context Protocol","Workflow Automation","Custom Node"],"keywords":["TypeScript","n8n","AI","Automation","Model Context Protocol","Workflow Automation","Custom Node"],"stars":null,"summary":"The n8n-nodes-mcp is a custom n8n node designed to facilitate interaction with Model Context Protocol (MCP) servers. It empowers n8n workflows to connect with AI models, access resources, execute tools, and utilize prompts in a standardized manner, significantly enhancing AI agent capabilities.","content":"## Introduction\n\nThe `n8n-nodes-mcp` repository provides a custom node for n8n, a fair-code licensed workflow automation platform, enabling seamless integration with Model Context Protocol (MCP) servers. MCP is a powerful protocol designed to allow AI models to interact with external tools and data sources in a standardized and efficient manner. This node empowers your n8n workflows to connect to MCP servers, access various resources, execute tools, and utilize prompts, significantly extending the capabilities of AI agents within your automations.\n\nWith **2855 stars** and **494 forks**, this MIT-licensed project, primarily developed in **TypeScript**, demonstrates strong community interest and active development.\n\n## Installation\n\nTo integrate the MCP Client node into your n8n instance, follow the official [installation guide for n8n community nodes](https://docs.n8n.io/integrations/community-nodes/installation/) (opens in a new tab).\n\nIt is crucial to note that if you intend to use the MCP Client node as a tool within n8n AI Agents, you must set the `N8N_COMMUNITY_PACKAGES_ALLOW_TOOL_USAGE` environment variable to `true`. This enables community nodes to function as tools for your AI agents.\n\n## Examples\n\nThe `n8n-nodes-mcp` node offers flexible ways to connect and interact with MCP servers, supporting various transport methods and configurations.\n\n### Connection Types and Credentials\n\nThe node supports three primary connection types:\n\n*   **Command-line Based Transport (STDIO):** Configure the command, arguments, and environment variables to start an MCP server directly.\n*   **HTTP Streamable Transport (Recommended):** Connect to an HTTP endpoint that supports streaming responses. This is the modern and recommended method for new implementations.\n*   **Server-Sent Events (SSE) Transport (Deprecated):** Available for legacy compatibility, connecting to SSE endpoints. HTTP Streamable is preferred for new projects.\n\n#### Example: Using a Local MCP Server with HTTP Streamable\n\n1.  Start a local MCP server that supports HTTP Streamable:\n    bash\n    npx @modelcontextprotocol/server-example-streamable\n    \n2.  In n8n, configure new credentials of type **MCP Client (HTTP Streamable) API**.\n3.  Set **HTTP Streamable URL** to `http://localhost:3001/stream`.\n4.  Add an MCP Client node to your workflow, select `HTTP Streamable` as the Connection Type, and choose your newly created credentials.\n\n### Environment Variables\n\nEnvironment variables can be passed to MCP servers via the credentials UI or, for Docker deployments, by prefixing them with `MCP_`.\n\n#### Example: Using Brave Search MCP Server\n\nThis example demonstrates integrating the Brave Search MCP server:\n\n1.  Install the Brave Search MCP server globally:\n    bash\n    npm install -g @modelcontextprotocol/server-brave-search\n    \n2.  Configure MCP Client credentials in n8n:\n    *   **Command**: `npx`\n    *   **Arguments**: `-y @modelcontextprotocol/server-brave-search`\n    *   **Environment Variables**: `BRAVE_API_KEY=your-api-key`\n3.  In an n8n workflow, add an MCP Client node. First, use the \"List Tools\" operation to see available search tools. Then, add another MCP Client node, select the \"Execute Tool\" operation, choose \"brave_search\", and set parameters like `{\"query\": \"latest AI news\"}`.\n\n### Multi-Server Setup with AI Agent\n\nFor advanced scenarios, you can configure multiple MCP servers in a Docker environment and leverage them with n8n's AI Agent capabilities. By setting `MCP_` prefixed environment variables in your `docker-compose.yml` and creating corresponding MCP Client credentials in n8n, an AI Agent can utilize various tools from different MCP servers.\n\nyaml\nversion: '3'\n\nservices:\n  n8n:\n    image: n8nio/n8n\n    environment:\n      # MCP server environment variables\n      - MCP_BRAVE_API_KEY=your-brave-api-key\n      - MCP_OPENAI_API_KEY=your-openai-key\n      # ... other MCP keys\n      - N8N_COMMUNITY_PACKAGES_ALLOW_TOOL_USAGE=true\n    ports:\n      - \"5678:5678\"\n    volumes:\n      - ~/.n8n:/home/node/.n8n\n\n\n### Operations\n\nThe MCP Client node supports several operations to interact with MCP servers:\n\n*   **Execute Tool:** Run a specific tool with defined parameters.\n*   **Get Prompt:** Retrieve a specific prompt template.\n*   **List Prompts:** Get a list of all available prompts.\n*   **List Resources:** Obtain a list of resources from the MCP server.\n*   **List Tools:** Discover all available tools, including their descriptions and parameter schemas.\n*   **Read Resource:** Read a specific resource by its URI.\n\n## Why Use n8n-nodes-mcp?\n\nThe `n8n-nodes-mcp` node is an essential tool for anyone looking to enhance their n8n workflows with advanced AI capabilities. It provides a standardized and robust way to:\n\n*   **Integrate AI Models:** Connect your n8n automations directly to AI models via the Model Context Protocol.\n*   **Access External Tools & Data:** Empower AI agents to use external tools and data sources, such as search engines or weather APIs, within your workflows.\n*   **Streamline AI Agent Development:** Simplify the creation of sophisticated AI agents by providing a unified interface for various MCP servers.\n*   **Leverage Community Resources:** Benefit from a growing community, extensive documentation, and video tutorials that guide you through setup and advanced usage.\n*   **Future-Proof Integrations:** Utilize the recommended HTTP Streamable transport for efficient and flexible communication with MCP servers.\n\n## Links\n\n*   **GitHub Repository:** [https://github.com/nerding-io/n8n-nodes-mcp](https://github.com/nerding-io/n8n-nodes-mcp) (opens in a new tab)\n*   **n8n community nodes documentation:** [https://docs.n8n.io/integrations/community-nodes/installation/](https://docs.n8n.io/integrations/community-nodes/installation/) (opens in a new tab)\n*   **Model Context Protocol Documentation:** [https://modelcontextprotocol.io/docs/](https://modelcontextprotocol.io/docs/) (opens in a new tab)\n*   **MCP TypeScript SDK:** [https://github.com/modelcontextprotocol/typescript-sdk](https://github.com/modelcontextprotocol/typescript-sdk) (opens in a new tab)\n*   **MCP Transports Overview:** [https://modelcontextprotocol.io/docs/concepts/transports](https://modelcontextprotocol.io/docs/concepts/transports) (opens in a new tab)\n*   **Official Quickstart Video:** [https://youtu.be/1t8DQL-jUJk](https://youtu.be/1t8DQL-jUJk) (opens in a new tab)\n*   **MCP Explained YouTube Series:** [https://www.youtube.com/playlist?list=PLjOCx_PNfJ4S_oOSqrMi6t9_x1GllvQZO](https://www.youtube.com/playlist?list=PLjOCx_PNfJ4S_oOSqrMi6t9_x1GllvQZO) (opens in a new tab)","metrics":{"detailViews":3,"githubClicks":12},"dates":{"published":null,"modified":"2025-11-01T08:01:30.000Z"}}