What dependencies or runtime are required?
Node.js, npm, and your n8n instance URL plus an API key are required. The server is built with TypeScript and uses SQLite local databases.
@jasondsmith72
Basic information
MCP Server for interacting with an n8n instance
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"n8n-api-mcp": {
"command": "node",
"args": [
"C:\\MCP-Servers\\N8N-api-MCP\\build\\index.js"
],
"env": {
"N8N_URL": "",
"N8N_API_KEY": ""
}
}
}
}Search the local API spec database for endpoints matching a query (path, summary, description, tags).
Retrieve detailed information (parameters, request body, responses) for a specific endpoint (path and method) from the local API spec database.
Execute an API call to the configured n8n instance. Checks Fast Memory first; if not found, prompts to save the call to Fast Memory upon success.
Search for API calls using natural language. Checks Fast Memory first, then falls back to searching the API spec database based on the query.
Manually save an API call (path, method, params, data) along with its original natural language query and a description to the Fast Memory database.
List entries currently stored in the Fast Memory database, optionally filtering by a search term.
Delete a specific entry from the Fast Memory database by its ID.
Remove all entries from the Fast Memory database.
Load or update the API Specification database (`api_spec.db`) by parsing an OpenAPI/Swagger JSON file.
Execute an API call using a raw request string (e.g., "GET /workflows?limit=5"). Uses the same underlying logic as `execute_api_call`, including Fast Memory checks and save prompts.
An MCP server that interacts with a self-hosted n8n instance, enabling API calls, searching endpoints via a local database populated from an OpenAPI spec, and storing frequently used calls in a Fast Memory database for quicker natural language access.
Clone the repository, install dependencies (npm install), build the server (npm run build), and configure environment variables (N8N_URL, N8N_API_KEY) in your MCP client’s settings (e.g., cline_mcp_settings.json). Then load the API spec using load_api_spec_from_json before using search tools.
send_raw_api_request).Node.js, npm, and your n8n instance URL plus an API key are required. The server is built with TypeScript and uses SQLite local databases.
It is loaded from a downloaded OpenAPI/Swagger JSON file using the `load_api_spec_from_json` tool. This file may be available from your n8n instance at `/api/v1/docs-json`.
It caches successful API calls linked to natural language queries. On a successful call via `execute_api_call` or `send_raw_api_request` (where the call is not already in Fast Memory), the server prompts to save it. It supports manual save, list, delete, and clear operations.
The server communicates via standard MCP protocol but the README does not specify a particular transport layer (stdio or HTTP). Configuration examples show a `command: "node"` setup typical for stdio transport.
The README does not mention any specific limits. The server is designed for self-hosted n8n instances and requires the user to provide the URL and API key.

SSH MCP server for AI agents: remote commands, file transfer, log search and server audits through OpenSSH.

Catch intent conflicts before code conflicts. The open-source coordination protocol for coding agents, built above Git.

Local-first credential broker for MCP. AI coding agents (Claude Code, Cursor, Codex…) call APIs, SSH and databases through scoped presets; secrets stay in your OS keychain and never enter the agent's context.

Turn a plain-language guide into a narrated walkthrough video of your deployed web app. Your coding agent can write the guide from the source and bring back the MP4.

Multi-zone MCP server for GKE/EKS/AKS: Python tools from git, NeMo guardrails, SSO, autoscaling.
Comments