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MCP Client Tool node#

The MCP Client Tool node is a Model Context Protocol (MCP) client, allowing you to use the tools exposed by an external MCP server. You can connect the MCP Client Tool node to your models to call external tools with n8n agents.

Credentials

The MCP Client Tool node supports both Bearer and generic header authentication methods.

Node parameters#

Configure the node with the following parameters.

  • SSE Endpoint: The SSE endpoint for the MCP server you want to connect to.
  • Authentication: The authentication method for authentication to your MCP server. The MCP tool supports bearer and generic header authentication. Select None to attempt to connect without authentication.
  • Tools to Include: Choose which tools you want to expose to the AI Agent:
    • All: Expose all the tools given by the MCP server.
    • Selected: Activates a Tools to Include parameter where you can select the tools you want to expose to the AI Agent.
    • All Except: Activates a Tools to Exclude parameter where you can select the tools you want to avoid sharing with the AI Agent. The AI Agent will have access to all MCP server's tools that aren't selected.

Templates and examples#

Build an MCP Server with Google Calendar and Custom Functions

by Solomon

View template details
Build an MCP Server with Google Calendar

by GuanNan

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AI-Powered Telegram Task Manager with MCP Server

by Francis Njenga

View template details
Browse MCP Client Tool integration templates, or search all templates

n8n also has an MCP Server Trigger node that allows you to expose n8n tools to external AI Agents.

Refer to the MCP documentation and MCP specification for more details about the protocol, servers, and clients.

Refer to LangChain's documentation on tools for more information about tools in LangChain.

View n8n's Advanced AI documentation.

AI glossary#

  • completion: Completions are the responses generated by a model like GPT.
  • hallucinations: Hallucination in AI is when an LLM (large language model) mistakenly perceives patterns or objects that don't exist.
  • vector database: A vector database stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.
  • vector store: A vector store, or vector database, stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.