MCP Servers for Prometheus

Prometheus is an open-source systems monitoring and alerting toolkit. It collects and stores metrics as time series data, with flexible queries and real-time alerting.

View all MCP Servers

  • Why this server?

    Enables monitoring of the MCP server using Prometheus-compatible clients.

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    An MCP server to interact with a Tinybird Workspace from any MCP client.
    Last updated -
    51
    Python
    Apache 2.0
    • Apple
  • Why this server?

    Provides access to Prometheus metrics and queries, allowing execution of PromQL queries, metrics discovery and exploration, viewing instant and range query results, and retrieving target information from a Prometheus server.

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    Provides access to Prometheus metrics and queries through standardized Model Context Protocol interfaces, allowing AI assistants to execute PromQL queries and analyze metrics data.
    Last updated -
    5
    63
    Python
    MIT License
    • Linux
    • Apple
  • Why this server?

    Provides tools for monitoring and metrics collection through Prometheus, allowing for querying and visualization of time-series data.

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    A proof-of-concept Prometheus MCP server, which likely enables Claude AI to interact with Prometheus monitoring systems through the Model Context Protocol.
    Last updated -
    2
    Python
    MIT License
  • Why this server?

    Allows querying Prometheus data sources, retrieving metric metadata, listing metric names, and exploring label names and values to analyze time series data.

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    A Model Context Protocol (MCP) server for Grafana. This provides access to your Grafana instance and the surrounding ecosystem.
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    703
    Go
    Apache 2.0
    • Linux
    • Apple
  • Why this server?

    Enables querying and monitoring metrics through Prometheus, including instant queries, range queries, alerts, and targets

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    A server implementation that provides a unified interface for OpenAI services, Git repository analysis, and local filesystem operations through REST API endpoints.
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    Python
    GPL 3.0
    • Linux
    • Apple
  • Why this server?

    Enables data retrieval and analysis from Prometheus databases, including fetching specific metrics, performing statistical analysis, searching metric usage patterns, and executing complex PromQL queries for in-depth data exploration.

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    An MCP server that enables Large Language Models to retrieve, analyze, and query metric data from Prometheus databases through pre-defined routes.
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    24
    Python
    MIT License
    • Linux
    • Apple
  • Why this server?

    Collects metrics from AI model operations, enabling detailed monitoring of performance, usage patterns, and resource utilization.

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    A high-performance FastAPI server supporting Model Context Protocol (MCP) for seamless integration with Large Language Models, featuring REST, GraphQL, and WebSocket APIs, along with real-time monitoring and vector search capabilities.
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    5
    Python
    MIT License
  • Why this server?

    Allows to interact with Prometheus metrics and data, including listing and accessing metric schema, metadata, and statistical information (count, min, max values)

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    MCP server for interacting with Prometheus metrics and data.
    Last updated -
    8
    JavaScript
    MIT License
    • Apple
  • Why this server?

    Allows access to Prometheus monitoring data through an MCP server, supporting authentication via username/password or token, and compatibility with multi-tenant setups through organization ID specification.

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    A tool that lets users access Prometheus metrics data through a Model Context Protocol server, supporting authentication options for secure data retrieval.
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    Python
  • Why this server?

    Enables access to Prometheus monitoring and time-series data through an MCP server, allowing AI assistants to query metrics, analyze performance data, and interact with Prometheus instances.

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    A tool that enables access to Prometheus metrics data through a Model Context Protocol server, allowing interaction with monitoring data using natural language.
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    Python
  • Why this server?

    Enables comprehensive monitoring and metrics collection for the Sequential Questioning server

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    A specialized server that enables LLMs to gather specific information through sequential questioning, implementing the MCP standard for seamless integration with LLM clients.
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    Python
  • Why this server?

    Collects and stores monitoring metrics for the MCP server deployment

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    Provides a scalable, containerized infrastructure for deploying and managing Model Context Protocol servers with monitoring, high availability, and secure configurations.
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