Prometheus MCP Server
by MCP-Mirror
This is a Model Context Protocol (MCP) server designed to retrieve data from Prometheus databases. It enables Large Language Models (LLMs) to interact with Prometheus metrics through pre-defined routes.
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What is Prometheus MCP Server?
The Prometheus MCP Server is a tool that allows Large Language Models (LLMs) to access and analyze data stored in Prometheus databases. It acts as an intermediary, providing a structured interface for LLMs to query and retrieve metrics, enabling them to perform tasks such as data analysis, usage search, and complex querying.
How to use Prometheus MCP Server?
To use the server, you need to set up a Python virtual environment, install the required packages from requirements.txt, and configure the MCP client (like Claude Desktop) with the server's command and environment variables. Alternatively, you can start the server standalone using uv or python3 server.py.
Key features of Prometheus MCP Server
Data Retrieval from Prometheus
Metric Analysis
Usage Search
Complex Querying with PromQL
Use cases of Prometheus MCP Server
Analyzing metric data for performance monitoring
Searching for specific metric usage patterns
Executing complex PromQL queries for in-depth data exploration
Integrating Prometheus data with LLMs for automated insights
FAQ from Prometheus MCP Server
What is MCP?
What is MCP?
Model Context Protocol (MCP) is a protocol that allows LLMs to invoke tool functions.
What is Prometheus?
What is Prometheus?
Prometheus is a popular open-source monitoring solution.
How do I configure the server for Claude Desktop?
How do I configure the server for Claude Desktop?
You need to modify the claude_desktop_config.json file with the appropriate command, arguments, and environment variables.
What if pip is not installed in my venv?
What if pip is not installed in my venv?
You can manually install pip using the provided get-pip.py script.
How can I contribute to this project?
How can I contribute to this project?
You can contribute by forking the repository, creating a feature branch, committing your changes, and opening a pull request.