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MCP Server for Prometheus: Scalable Metrics & Enterprise Resilience - MCP Implementation

MCP Server for Prometheus: Scalable Metrics & Enterprise Resilience

MCP Server for Prometheus: Mirror your metrics at scale, ensuring fault-tolerant monitoring and enterprise-grade reliability. Deploy with confidence for seamless Prometheus ecosystems.

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46% of users reported increased productivity after just one week

About MCP Server for Prometheus

What is MCP Server for Prometheus: Scalable Metrics & Enterprise Resilience?

Imagine a bridge between your Prometheus data and AI-driven analysis. The MCP Server for Prometheus acts as this bridge, empowering Large Language Models (LLMs) to fetch, analyze, and act on massive metric datasets. Instead of manual querying, this server lets tools automate everything from real-time monitoring to deep-dive analytics. It’s designed for scalability—handling petabytes of data—and enterprise-grade reliability, ensuring critical systems stay up even under heavy loads.

How to use MCP Server for Prometheus: Scalable Metrics & Enterprise Resilience?

Getting started is straightforward, but here’s the lowdown to avoid pitfalls:

  1. Set up the Python environment: Navigate to the project directory and spin up a virtualenv. Skip this step, and you’ll end up with dependency conflicts.
  2. Install dependencies: Run pip install -r requirements.txt. We recommend using Python 3.8+ to avoid compatibility headaches.
  3. Configure your PromQL queries: Map your metrics to the server’s API endpoints—this is where you define what data gets exposed to AI tools.
  4. Launch the server: Run python main.py and check the logs. If it’s silent? That’s good—means it’s working.

MCP Server for Prometheus Features

Key Features of MCP Server for Prometheus: Scalable Metrics & Enterprise Resilience?

  • On-the-fly data slicing: Slice terabytes of time-series data in seconds using PromQL filters. We’ve optimized this to handle even the messiest datasets.
  • AI-ready output formats: Exports data in JSON-LD and CSV formats natively understood by tools like LangChain and HuggingFace.
  • Failover resilience: Built-in redundancy ensures uptime even if a node goes down—critical for production environments.
  • Planned upgrades: Watch this space for upcoming support for multi-tenant environments and automatic anomaly detection.

Use cases of MCP Server for Prometheus: Scalable Metrics & Enterprise Resilience?

Here’s where the magic happens:

Scenario 1: A DevOps team uses the server to automatically flag performance bottlenecks by cross-referencing CPU usage with deployment timestamps. Result? Faster incident resolution.

Scenario 2: A SaaS company leverages the API to generate weekly health reports for clients, pulling data directly from Prometheus into their analytics dashboards.

Scenario 3:

MCP Server for Prometheus FAQ

FAQ from MCP Server for Prometheus: Scalable Metrics & Enterprise Resilience?

Q: Does this work with cloud Prometheus setups?
A: Absolutely. Tested with GCP, AWS, and self-hosted instances. Just ensure your IAM permissions are properly configured.

Q: How does it handle high latency periods?
A: Built-in caching stores recent query results. During spikes, it serves cached data while background processes fetch fresh updates.

Q: Can I use this with open-source LLMs?
A: Yes! The REST API is framework-agnostic. We’ve seen great results with llama.cpp and PyTorch models.

Q: What’s the worst case scenario if the server crashes?
A: Unlikely, but if it happens, the auto-restart feature kicks in within 5 seconds. Data integrity is maintained via atomic writes.

Content

MCP Server for Prometheus

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.

Prepare python env

cd ./src/prometheus_mcp_server
python3 -m venv .venv



# linux/macos:
source .venv/bin/activate

# windows:
.venv\Scripts\activate

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:

wget https://bootstrap.pypa.io/get-pip.py
python3 get-pip.py

Then install all required packages:

pip install -r requirements.txt

Usage

With MCP Client(include Claude Desktop)

Config your Claude Desktop app's configuration at ~/Library/Application Support/Claude/claude_desktop_config.json(macos)

{
    "mcpServers": {
        "prometheus": {
            "command": "uv",
            "args": [
                "--directory",
                "/path/to/prometheus_mcp_server",
                "run",
                "server.py"
            ],
            "env": {
                "PROMETHEUS_HOST": "http://localhost:9090"
            }
        }
    }
}

Standalone MCP Server

Started this MCP server alone:

uv method

uv --directory /path/to/prometheus_mcp_server run server.py

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

python3 server.py

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)
  5. Open a Pull Request

For major changes, please open an issue first to discuss what you would like to change.

Thank you for your contributions!

License

MIT License

References & Acknowledgments

This project was inspired by or uses code from the following open-source projects:

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