The official MCP server and Python SDK for the deepset AI platform
deepset-mcp enables AI agents to build and debug pipelines on the Haystack Enterprise AI platform through 30+ specialized tools. It also provides a Python SDK for programmatic access to many platform resources.
Install the project using uv:
# Install uv first
pipx install uv
# Install project with all dependencies
uv sync --locked --all-extras --all-groupsIf you want to test your changes locally, follow these steps:
- Add a script run-deepset-mcp.sh that uses the binary from the project's virtual env
#!/usr/bin/env bash
# Wrapper to run the local deepset-mcp server for Cursor MCP.
# Use this as command so it doesn't depend on uv or PATH.
set -e
cd "$(dirname "$0")"
exec .venv/bin/deepset-mcp- Use it this way in Cursor:
"deepset": {
"command": "/bin/bash",
"args": ["/Users/*****/****/deepset-mcp-server/run-deepset-mcp.sh"],
"cwd": "/Users/*****/****/deepset-mcp-server",
"env": {
"DEEPSET_WORKSPACE": "WORKSPACE",
"DEEPSET_API_KEY": "API_KEY"
}
}Note: If you change the codebase, make sure to restart the MCP server.
Run code quality checks and tests using the Makefile:
# Install dependencies
make install
# Code quality
make lint # Run ruff linting
make format # Format code with ruff
make types # Run mypy type checking
# Testing
make test # Run unit tests (default)
make test-unit # Run unit tests only
make test-integration # Run integration tests
make test-all # Run all tests
# Clean up
make clean # Remove cache filesDocumentation is built using MkDocs with the Material theme:
- Configuration:
mkdocs.yml - Content:
docs/directory - Auto-generated API docs via mkdocstrings
- Deployed via GitHub Pages (automated via GitHub Actions on push to main branch)