CaesarYangs/prometheus_mcp_server

A Model Context Protocol (MCP) server for retrieving data from Prometheus databases. This MCP server enables Large Language Models (LLMs) to invoke tool functions that retrieve and analyze vast amounts of metric data, search metric usage, execute complex queries, and perform other related tasks through pre-defined routes with enhanced control over usage.
- Data Retrieval: Fetch specific metrics or ranges of data from Prometheus.
- Metric Analysis: Perform statistical analysis on retrieved metrics.
- Usage Search: Find and explore metric usage patterns.
- Complex Querying: Execute advanced PromQL queries for in-depth data exploration.
Capibilites
✅ Retrieve comprehensive metric information, including names and descriptions, from Prometheus
✅ Fetch and analyze specific metric data using metric names
✅ Analyze metric data within custom time ranges
🚧 Filter and match data using specific labels (in development)
⏳ Additional features planned...
Getting Started
MCP runing requires a python virtual environment(venv), all packages should be installed into this venv so the MCP server can be automically started.
Installing via Smithery
To install Prometheus MCP Server for Claude Desktop automatically via Smithery:
Manual Installation
**Prepare python env**
Then it is ready to be used as a dedicated python environment.
**Install required packages**
Make sure pip is properly isntalled. If your venv is installed without pip, then manually install it using:
Then install all required packages:
Usage
With Cursor Env
Ready to update depend on more easy-to-use Cursor environment.
Set this in the MCP section in Cursor Settings:
With MCP Client(include Claude Desktop)
Config your Claude Desktop app's configuration at ~/Library/Application Support/Claude/claude_desktop_config.json(macos)
Standalone MCP Server
Started this MCP server alone:
**uv method**
This is also a way to make sure this MCP server can be automatically started since the Claude Desktop is using this ux script way to start when the app launches.
**regular python method**
Contributing
Contributions are welcome! Here's a quick guide:
1. Fork the repo
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)