What is N8n Mcp Server

n8n-mcp-server is a Model Context Protocol (MCP) server designed for integration with n8n automation. It allows users to control n8n workflows programmatically using Large Language Models (LLMs).

Use cases

Use cases for n8n-mcp-server include automating repetitive tasks, integrating multiple services, and simplifying workflow management through natural language processing.

How to use

To use n8n-mcp-server, install it according to the provided installation instructions, ensure you understand the token consumption, and then interact with n8n using natural language commands to create, modify, and manage workflows.

Key features

Key features of n8n-mcp-server include automated workflow creation and modification, manipulation of n8n nodes, and management of workflow executions, all facilitated through LLMs.

Where to use

n8n-mcp-server can be used in various fields such as automation, data processing, and integration tasks, particularly where complex workflows need to be managed efficiently.

n8n MCP Server

**⚠️ WARNING: ** Using this tool may consume a large number of tokens. Please ensure you understand the associated costs before using it.

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Table of Contents

• Project Overview
• Project Status
  • Completed Features
  • Pending Features
• Available Tools
  • Workflow Management
  • Execution Monitoring
  • Node Discovery & Analysis
• Installation & Usage
  • Installation
  • Security Considerations
• Technical Stack
• Project Structure
• Contribution Guidelines
• License
• Acknowledgments

Project Overview

This project establishes a Model Context Protocol (MCP) server for interaction with an n8n instance. It enables Large Language Models (LLMs) to programmatically control n8n for tasks such as:

• Automated creation and modification of n8n workflows.
• Manipulation of n8n nodes.
• Management of n8n workflow executions.

The primary objective is to allow users to direct an LLM via natural language to operate n8n, thereby simplifying and automating workflow design and management.

graph LR
    %% 定義 n8n API 節點（每個節點代表一組 API）
    wf[Workflows API]
    ex[Executions API]
    us[Users API]
    cr[Credentials API]
    tg[Tags API]
    pr[Projects API]
    va[Variables API]
    au[Audit API]
    sc[SourceControl API]
    
    %% 定義中樞節點與 MCP Tool
    n8n[("n8n-sdk-python")]
    tool[("n8n-mcp-server MCP Tool")]
    
    %% Workflows API 端點連線
    wf -- "GET /workflows" --> n8n
    wf -- "GET /workflows/:id" --> n8n
    wf -- "POST /workflows" --> n8n
    wf -- "PUT /workflows/:id" --> n8n
    wf -- "DELETE /workflows/:id" --> n8n

    %% Executions API 端點連線
    ex -- "GET /executions" --> n8n
    ex -- "GET /executions/:id" --> n8n
    ex -- "POST /executions" --> n8n

    %% Users API 端點連線
    us -- "GET /users" --> n8n
    us -- "GET /users/:id" --> n8n
    us -- "POST /users" --> n8n
    us -- "PUT /users/:id" --> n8n
    us -- "DELETE /users/:id" --> n8n

    %% Credentials API 端點連線
    cr -- "GET /credentials" --> n8n
    cr -- "GET /credentials/:id" --> n8n
    cr -- "POST /credentials" --> n8n
    cr -- "PUT /credentials/:id" --> n8n
    cr -- "DELETE /credentials/:id" --> n8n

    %% Tags API 端點連線
    tg -- "GET /tags" --> n8n
    tg -- "GET /tags/:id" --> n8n
    tg -- "POST /tags" --> n8n
    tg -- "DELETE /tags/:id" --> n8n

    %% Projects API 端點連線
    pr -- "GET /projects" --> n8n
    pr -- "GET /projects/:id" --> n8n
    pr -- "POST /projects" --> n8n
    pr -- "PUT /projects/:id" --> n8n
    pr -- "DELETE /projects/:id" --> n8n

    %% Variables API 端點連線
    va -- "GET /variables" --> n8n
    va -- "GET /variables/:id" --> n8n
    va -- "POST /variables" --> n8n
    va -- "PUT /variables/:id" --> n8n
    va -- "DELETE /variables/:id" --> n8n

    %% Audit API 端點連線
    au -- "GET /audit" --> n8n

    %% SourceControl API 端點連線
    sc -- "GET /sourceControl" --> n8n
    sc -- "GET /sourceControl/:id" --> n8n
    sc -- "POST /sourceControl" --> n8n
    sc -- "PUT /sourceControl/:id" --> n8n
    sc -- "DELETE /sourceControl/:id" --> n8n

    %% 中樞節點轉發至 MCP Tool
    n8n -- "forward request" --> tool

    %% 右側：MCP Tool 下的各工具子分組

    %% 工作流 workflow
    subgraph "Workflow"
        list_workflows[list_workflows]
        get_workflow[get_workflow]
        create_workflow[create_workflow]
        update_workflow[update_workflow]
        delete_workflow[delete_workflow]
        activate_workflow[activate_workflow]
        deactivate_workflow[deactivate_workflow]
    end

    %% 執行 execution
    subgraph "Execution"
        list_executions[list_executions]
        get_execution[get_execution]
        execute_workflow[execute_workflow]
        stop_execution[stop_execution]
        delete_execution[delete_execution]
        retry_execution[retry_execution]
    end

    %% 節點 node
    subgraph "Node"
        list_node_types[list_node_types]
        get_node_type[get_node_type]
        create_node[create_node]
        update_node[update_node]
        delete_node[delete_node]
    end

    %% 連線 connection
    subgraph "Connection"
        list_connections[list_connections]
        create_connection[create_connection]
        delete_connection[delete_connection]
    end

    %% 憑證 credential
    subgraph "Credential"
        list_credentials[list_credentials]
        get_credential[get_credential]
        create_credential[create_credential]
        update_credential[update_credential]
        delete_credential[delete_credential]
        test_credential[test_credential]
        list_credential_types[list_credential_types]
    end

    %% 從 MCP Tool 對各工具連線
    tool --> list_workflows
    tool --> get_workflow
    tool --> create_workflow
    tool --> update_workflow
    tool --> delete_workflow
    tool --> activate_workflow
    tool --> deactivate_workflow

    tool --> list_executions
    tool --> get_execution
    tool --> execute_workflow
    tool --> stop_execution
    tool --> delete_execution
    tool --> retry_execution

    tool --> list_node_types
    tool --> get_node_type
    tool --> create_node
    tool --> update_node
    tool --> delete_node

    tool --> list_connections
    tool --> create_connection
    tool --> delete_connection

    tool --> list_credentials
    tool --> get_credential
    tool --> create_credential
    tool --> update_credential
    tool --> delete_credential
    tool --> test_credential
    tool --> list_credential_types

Project Status

Completed Features

• Workflow Management:
  • List workflows with filtering capabilities.
  • Retrieve detailed workflow definitions.
  • Create new workflows with nodes, connections, settings, and static data.
  • Update existing workflows (name, nodes, connections, activation status, settings, static data).
  • Delete workflows.
  • Activate and deactivate workflows.
• Node Discovery & Analysis:
  • List available node types from local classification files (filtered by category/class).
  • Retrieve detailed node definition files for a specific node type.
• Execution Monitoring:
  • List workflow execution records with filtering.
  • Get detailed information for a specific execution.
  • Delete execution records.
• MCP Resources:
  • n8n:/workflow/{workflow_id}: Access workflow definition data.
  • n8n:/node-types: Access information on available n8n node types from local files.
  • n8n:/tags: Access information on all n8n tags.

Pending Features

• Reduce token consumption, optimize prompts.
• Execution Control:
  • execute_workflow: Programmatically trigger workflow executions.
  • stop_execution: Halt ongoing workflow executions.
  • retry_execution: Re-run failed workflow executions.
• Node Management (Workflow Context):
  • create_node: Add a new node to an existing workflow.
  • update_node: Modify an existing node within a workflow.
  • delete_node: Remove a node from a workflow.
• Connection Management (Workflow Context):
  • list_connections: List connections within a specific workflow.
  • create_connection: Add a connection between nodes in a workflow.
  • delete_connection: Remove a connection between nodes in a workflow.
• Credential Management:
  • Full suite of tools for listing, getting, creating, updating, deleting, and testing credentials.
  • Listing available credential types.
• Broader n8n API Coverage: Integration of tools for Users, Projects, Variables, Audit, and Source Control if deemed necessary.

Available Tools

The MCP server exposes the following tools for LLM interaction, based on the n8n-sdk-python:

Workflow Management

• list_workflows: Retrieves a filtered list of workflows from the n8n instance.

  • Description: Returns a collection of workflow summaries matching specified filter criteria. Each summary includes metadata like ID, name, active status, and tags. For full definitions, use get_workflow.
  • Args:
    • active_only (bool, optional): If True, returns only active workflows. Default: False.
    • tags (str, optional): Comma-separated string of tag names to filter by (e.g., “production,api”). Returns workflows with ALL specified tags.
    • name (str, optional): Filter by workflow name (partial matching).
    • project_id (str, optional): Filter by project ID.
    • limit (int, optional): Maximum number of workflows to return.
  • Returns (dict):
    • status (str): “success” or “failure”.
    • count (int): Number of workflows matching criteria.
    • workflows (list): Array of workflow summary objects.
    • message (str, optional): Error description on failure.

• get_workflow: Retrieves the complete definition of a specific workflow.

  • Description: Returns the full technical specification, including nodes, connections, settings, and metadata, conforming to n8n_sdk_python.models.workflows.Workflow.
  • Args:
    • workflow_id (str, required): The unique identifier of the workflow.
    • exclude_pinned_data (bool, optional): If True (default), omits pinned test data. If False, includes it.
  • Returns (dict):
    • status (str): “success” or “failure”.
    • workflow (dict): Complete workflow definition object on success.
    • message (str, optional): Error description on failure.

• create_workflow: Creates a new workflow with the specified configuration.

  • Description: Constructs a workflow adhering to n8n_sdk_python.models.workflows.WorkflowCreate.
  • Args:
    • name (str, required): Descriptive and unique workflow name.
    • nodes (list[dict], required): Array of node configurations (conforming to Node model).
    • connections (dict, optional): Data flow definitions between nodes.
    • active (bool, optional): Activate on creation. Default: False.
    • settings (dict, optional): Execution settings (timeout, data retention, timezone).
    • static_data (dict, optional): Persistent state storage.
  • Returns (dict):
    • status (str): “success” or “failure”.
    • message (str): Confirmation or error.
    • workflow (dict): Basic info of created workflow (id, name, active) on success.

• update_workflow: Modifies an existing workflow.

  • Description: Updates structure, settings, or activation. Omitted parameters retain current values.
  • Args:
    • workflow_id (str, required): Identifier of the workflow to modify.
    • name (str, optional): New display name.
    • nodes (list[dict], optional): Replaces ALL existing nodes if provided.
    • connections (dict, optional): Replaces ALL existing connections if provided.
    • active (bool, optional): New activation state.
    • settings (dict, optional): Updates execution settings (partial updates applied).
    • static_data (dict, optional): Replaces entire static data object.
  • Returns (dict):
    • status (str): “success” or “failure”.
    • message (str): Confirmation or error.
    • workflow (dict): Basic info of updated workflow (id, name, active) on success.

• delete_workflow: Permanently removes a workflow.

  • Description: Deletes workflow and all associated configurations. Irreversible.
  • Args:
    • workflow_id (str, required): Identifier of the workflow to delete.
  • Returns (dict):
    • status (str): “success” or “failure”.
    • message (str): Confirmation or error.

• activate_workflow: Enables a workflow for execution.

  • Description: Makes a workflow operational (webhook triggers, schedule triggers, event triggers become active).
  • Args:
    • workflow_id (str, required): Identifier of the workflow to activate.
  • Returns (dict):
    • status (str): “success” or “failure”.
    • message (str): Confirmation or error.
    • workflow (dict): Basic info (id, name, active) on success.

• deactivate_workflow: Disables a workflow from executing.

  • Description: Suspends operational capabilities (webhook, schedule, event triggers become inactive).
  • Args:
    • workflow_id (str, required): Identifier of the workflow to deactivate.
  • Returns (dict):
    • status (str): “success” or “failure”.
    • message (str): Confirmation or error.
    • workflow (dict): Basic info (id, name, active) on success.

Execution Monitoring

• list_workflow_executions: Retrieves execution history for a workflow.

  • Description: Returns a paginated list of execution records, filterable by status. Includes metadata like start/end times, status, and mode.
  • Args:
    • workflow_id (str, required): Target workflow identifier.
    • status (str, optional): Filter by outcome ('error', 'success', 'waiting').
    • limit (int, optional): Max records to return. Default: 10.
    • include_data (bool, optional): Include full execution data. Default: False.
  • Returns (dict):
    • status (str): “success” or “failure”.
    • count (int): Number of records returned.
    • executions (list): Array of execution record objects.
    • message (str, optional): Error description on failure.

• get_execution: Retrieves detailed information for a specific execution.

  • Description: Returns comprehensive details including context, timestamps, state, and optionally full execution data.
  • Args:
    • execution_id (str, required): Identifier of the execution record.
    • include_data (bool, optional): Include full execution data. Default: False.
  • Returns (dict):
    • status (str): “success” or “failure”.
    • execution (dict): Complete execution record on success.
    • message (str, optional): Error description on failure.

• delete_execution: Permanently removes an execution record.

  • Description: Deletes a specific execution record. Irreversible.
  • Args:
    • execution_id (str, required): Identifier of the execution record to delete.
  • Returns (dict):
    • status (str): “success” or “failure”.
    • message (str): Confirmation or error.

Node Discovery & Analysis

• list_nodes: Retrieves available node types from local classification files.

  • Description: Provides access to the node type registry, filterable by category or class. Queries local files.
  • Args:
    • category (str, optional): Filter by node category (case-insensitive, e.g., 'Core Nodes').
    • node_class (str, optional): Filter by node class (case-insensitive, e.g., 'action').
    • return_types_only (bool, optional): If True (default), returns array of node type identifiers. If False, returns detailed node objects.
  • Returns (dict):
    • status (str): “success” or “failure”.
    • count (int): Number of matching nodes.
    • nodes (list): Array of node type strings or detailed node objects.
    • message (str, optional): Error description on failure.

• get_node_info: Retrieves comprehensive definition for a specific node type.

  • Description: Provides detailed technical specifications (schema, parameters, implementation) by returning content of all definition files. Queries local files.
  • Args**:
    • node_type (str, required): Type identifier of the node (e.g., 'n8n-nodes-base.httpRequest').
  • Returns (dict):
    • status (str): “success” or “failure”.
    • files (dict): Maps filenames to content for all definition files on success.
    • message (str, optional): Error description on failure.
    • (Other metadata like type_identifier, display_name also included on success).

Setup and Usage

Installation and Configuration

1. Clone Repository and Install Dependencies

git clone https://github.com/eric050828/n8n-mcp-server.git
cd n8n-mcp-server
python -m venv venv
# On macOS/Linux
source venv/bin/activate
# On Windows
# venv\\Scripts\\activate
pip install -r requirements.txt

2. Configure Environment Variables
   Copy .env.example to .env and set the following critical variables:

cp .env.example .env

Edit .env:

# n8n API Configuration
N8N_BASE_URL=http://localhost:5678       # URL of your n8n instance
N8N_API_KEY=your_n8n_api_key_here       # n8n API Key (if authentication is enabled)

# Logging Configuration
LOG_LEVEL=INFO                          # Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)

# Node Data Paths (Ensure these paths are correct for your setup if not using defaults)
NODE_DATA_BASE_PATH=node_data
CATEGORY_CLASSIFICATION_PATH=node_data/category_classification_result.json
CLASS_CLASSIFICATION_PATH=node_data/class_classification_result.json

3. Add MCP Server to MCP Client

{
  "mcpServers": {
    "n8n-mcp-server": {
      "command": "path-to-your-project-root/venv/Scripts/python.exe",
      "args": [
        "path-to-your-project-root/n8n_mcp_server.py"
      ],
      "env": {
        "PYTHONPATH": "path-to-your-project-root",
        "N8N_BASE_URL": "http://localhost:5678",
        "N8N_API_KEY": "your_n8n_api_key",
        "NODE_DATA_BASE_PATH": "path-to-your-project-root/node_data",
        "CATEGORY_CLASSIFICATION_PATH": "path-to-your-project-root/node_data/category_classification_result.json",
        "CLASS_CLASSIFICATION_PATH": "path-to-your-project-root/node_data/class_classification_result.json"
      }
    }
  }
}

Security Considerations

1. API Key Protection: Secure your n8n API key; avoid exposing it in public environments.
2. Access Control: If deploying the MCP server over HTTP, implement robust authentication and authorization mechanisms to restrict access to authorized clients.
3. Operation Scoping: Configure tool usage permissions if your MCP client or gateway supports it, minimizing operational risks.
4. Logging and Auditing: Maintain detailed logs to track API calls and operations for security and debugging purposes.

Technical Stack

• Core Framework:
  • Python 3.8+
  • mcp.server.fastmcp.FastMCP for MCP server implementation.
  • n8n-sdk-python for interacting with the n8n API.
• Data Handling & Validation:
  • Pydantic (used by n8n-sdk-python and potentially within MCP tools/prompts).
• Environment Management:
  • python-dotenv for loading environment variables.
• Logging:
  • Standard Python logging module, with optional RichHandler for enhanced console output.

Project Structure

n8n-mcp-server/
├── mcp_components/ # Core MCP logic
│ ├── tools/ # MCP tool definitions
│ │ ├── init.py
│ │ ├── executions.py
│ │ ├── nodes.py
│ │ └── workflows.py
│ ├── resources.py # MCP resource definitions
│ ├── prompts.py # MCP prompt definitions
│ └── init.py
├── node_data/ # (Example) Local n8n node definition data
│ ├── category_classification_result.json # Generated node classification
│ ├── class_classification_result.json # Generated node classification
│ ├── categories/ # Node definitions by category
│ └── classes/ # Node definitions by class
├── config.py # Server configuration, n8n client initialization
├── main.py # Main server entry point
├── mcp_server.py # FastMCP app instance creation and lifespan management
├── .env # Environment variables
├── .env.example # Example environment variables
├── .gitignore
├── README.md
└── requirements.txt # Python dependencies

Contribution Guidelines

Contributions are welcome! Please follow these guidelines:

Reporting Issues

If you find a bug or have a feature suggestion, please open an issue on the GitHub repository, providing:

• A clear description of the issue or suggestion.
• Steps to reproduce (if applicable).
• Expected vs. actual behavior.
• Environment details (OS, Python version, etc.).

Code Contributions

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/your-amazing-feature).
3. Commit your changes (git commit -m \'Add some amazing feature\').
4. Push to the branch (git push origin feature/your-amazing-feature).
5. Open a Pull Request.

Development Standards

• Follow PEP 8 coding style.
• Write unit tests for new features.
• Update relevant documentation (README, docstrings).
• Ensure tests pass before submitting (pytest).

License

This project is licensed under the MIT License - see the LICENSE file for details (if one is created).

Acknowledgements

• The n8n team for the powerful workflow automation platform.
• The Model Context Protocol initiative for standardizing AI-tool interaction.
• Contributors to n8n-sdk-python.