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n8n Advisor

n8n Advisor

by m2ai-mcp-servers
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

n8n Advisor

MCP server for n8n workflow management. Provides AI assistants with the ability to review workflow status in n8n instances.

Features

  • check_workflows: Reviews all workflows and reports their status, including active/inactive counts and workflows with recent errors.

Related MCP server: n8n MCP Server

Installation

pip install m2ai-mcp-n8n-advisor

Configuration

Create a .env file or set environment variables:

# Base URL of your n8n instance (no trailing slash)
N8N_BASE_URL=https://your-n8n-instance.app.n8n.cloud

# API key from n8n Settings > API
N8N_API_KEY=your_api_key_here

Getting Your n8n API Key

  1. Open your n8n instance

  2. Go to Settings > API

  3. Create a new API key or copy an existing one

  4. Use the full URL of your n8n instance for N8N_BASE_URL

Usage

Claude Desktop Configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "n8n": {
      "command": "n8n-advisor",
      "env": {
        "N8N_BASE_URL": "https://your-n8n-instance.app.n8n.cloud",
        "N8N_API_KEY": "your_api_key_here"
      }
    }
  }
}

Or run directly with Python:

{
  "mcpServers": {
    "n8n": {
      "command": "python",
      "args": ["-m", "n8n_mcp.server"],
      "env": {
        "N8N_BASE_URL": "https://your-n8n-instance.app.n8n.cloud",
        "N8N_API_KEY": "your_api_key_here"
      }
    }
  }
}

Available Tools

check_workflows

Reviews all workflows and reports their status.

Parameters: None

Returns:

  • total_workflows: Total number of workflows

  • active_workflows: Number of active workflows

  • inactive_workflows: Number of inactive workflows

  • workflows_with_errors: Count of workflows with recent errors

  • error_workflows: List of workflows with errors (id, name, error count)

  • workflows: Detailed list of all workflows with execution stats

Example Response:

{
  "total_workflows": 5,
  "active_workflows": 3,
  "inactive_workflows": 2,
  "workflows_with_errors": 1,
  "error_workflows": [
    {"id": "2", "name": "Data Sync", "errors": 3}
  ],
  "workflows": [
    {
      "id": "1",
      "name": "Email Notifications",
      "active": true,
      "recent_executions": {
        "success": 10,
        "error": 0,
        "waiting": 0,
        "running": 0
      }
    }
  ]
}

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=n8n_mcp

# Type checking
mypy src/

# Linting
ruff check src/

License

MIT

Available Tools

1 tool
check_workflowsA

Reviews all workflows and reports status. Shows total count, active/inactive breakdown, and identifies workflows with recent errors.

Inputs: no named parameters.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4/5.0

    Scored across 1 tool

    Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion or ambiguity between tools.

    Naming Consistency5/5

    With a single tool, naming is trivially consistent and follows a logical verb_noun pattern.

    Tool Count1/5

    A single read-only 'check' tool is insufficient for an 'Advisor' server, which implies broader capabilities like querying, analyzing, or modifying workflows.

    Completeness1/5

    The tool surface is severely incomplete: it offers only one check operation with no ability to act on workflows, retrieve details, or perform any state changes.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

    Resources

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