AIBase
EN
Explore
MCP Prometheus
M

MCP Prometheus

A Model Context Protocol server for Prometheus integration, providing native Go binaries, supporting automatic Kubernetes connection through client-go, and including 11 tools for querying, cluster diagnosis, and resource analysis.
MonitoringDeveloper tools#Monitoring tool#Kubernetes integration#Prometheus query#Cluster diagnosis.Go
2 points
6.2K
Open Site

Overview

Installation

Content Details

Alternatives

What is the Prometheus MCP Server?

The Prometheus MCP Server is an intelligent monitoring assistant that connects to your Prometheus monitoring system, allowing you to query the cluster status using natural language. Whether you are an operations engineer, developer, or system administrator, you can obtain real-time monitoring data for Kubernetes/OpenShift clusters through simple conversations without writing complex PromQL queries.

How to use the Prometheus MCP Server?

Simply configure the server address in a client that supports the MCP protocol (such as VS Code, Cursor, Windsurf, etc.) to start using it. The server will automatically connect to your Prometheus instance, whether through direct URL access or automatic discovery via the Kubernetes API. After configuration, you can directly ask questions in natural language, and the system will automatically convert them into monitoring queries and return the results.

Applicable scenarios

Suitable for operations teams that need to quickly diagnose cluster issues, developers who need to monitor application performance, and system administrators who need to regularly check the system health status. Particularly suitable for use in scenarios such as troubleshooting, performance optimization, capacity planning, and daily inspections.

Main features

Intelligent query conversion
Automatically convert natural language questions into PromQL queries without manually writing complex monitoring query statements
Automatic Kubernetes connection
Automatically detect and connect to the Prometheus service in the Kubernetes/OpenShift cluster without manually configuring port forwarding
Cluster health diagnosis
Provide comprehensive cluster health checks, including node status, resource usage, service availability, etc.
Resource consumption analysis
Identify the main consumers of resources such as CPU, memory, and network to help optimize resource allocation
Time range comparison
Compare monitoring data from different time periods to analyze trend changes and abnormal patterns
In-depth Pod investigation
Conduct a detailed analysis of specific Pods, including resource usage, performance metrics, and associated events
Multi-environment support
Support two connection methods: direct URL connection and automatic Kubernetes discovery, adapting to different deployment environments
Native Go implementation
Developed based on the Go language, with high performance, low resource consumption, and support for cross-platform deployment
Advantages
No need to learn PromQL: Query monitoring data through natural language, reducing the usage threshold
Automatic Kubernetes integration: Automatically discover the Prometheus service in the cluster, simplifying the configuration process
Comprehensive diagnostic tools: Provide 11 professional tools, covering various needs from basic queries to in-depth diagnosis
High-performance native implementation: Developed based on the Go language, with fast response and low resource consumption
Flexible deployment options: Support quick startup with npx, binary deployment, and containerized deployment
Open source and free: Under the MIT license, can be freely used and modified
Limitations
Depends on Prometheus: Requires an existing Prometheus monitoring system and cannot run independently
Requires an MCP client: Must be configured and used in an editor that supports the MCP protocol
Network connection requirements: Requires a network connection that can access the Prometheus API
Kubernetes permissions: The automatic connection function requires corresponding Kubernetes access permissions
Natural language understanding limitations: Complex queries may require multiple interactions to accurately understand the intention

How to use

Select the installation method
Choose a suitable installation method according to your environment: quick startup with npx, download the binary file, or use the container image
Configure the MCP client
Add the server configuration in your MCP client (such as VS Code, Cursor, etc.). If Prometheus is in the standard location, it can be fully automatically configured
Configure the connection method
Choose the connection method according to your environment: direct URL connection or automatic Kubernetes discovery. The Kubernetes method will automatically detect Prometheus in the cluster
Start querying
In the MCP client, use natural language to ask any questions about cluster monitoring

Usage examples

Daily cluster health check
Check the overall health status of the cluster every morning to quickly discover potential issues
Performance issue diagnosis
Quickly locate resource bottlenecks when the application response slows down
Capacity planning analysis
Analyze the current resource usage patterns and trends before planning resource expansion
Troubleshooting support
Quickly obtain relevant monitoring data when a node or service has a problem

Frequently Asked Questions

Do I need to install Prometheus to use this server?
Which MCP clients are supported?
What permissions are required for automatic Kubernetes connection?
How to customize the Prometheus connection address?
Does it support the high-availability deployment of Prometheus?
What should I do if the query response is slow?

Related resources

GitHub repository
Source code, issue tracking, and latest version releases
Model Context Protocol official website
Official documentation and specifications of the MCP protocol
Prometheus official documentation
Complete documentation of the Prometheus monitoring system
Container image
Docker/Podman container image address
Helm Chart
Helm chart for Kubernetes/OpenShift deployment

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "prometheus": {
      "command": "npx",
      "args": ["-y", "mcp-prometheus@latest"],
      "env": {
        "PROMETHEUS_URL": "http://localhost:9090"
      }
    }
  }
}

{
  "mcpServers": {
    "prometheus": {
      "command": "npx",
      "args": ["-y", "mcp-prometheus@latest"]
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

Alternatives

V
Vestige
Vestige is an AI memory engine based on cognitive science. By implementing 29 neuroscience modules such as prediction error gating, FSRS - 6 spaced repetition, and memory dreaming, it provides long - term memory capabilities for AI. It includes a 3D visualization dashboard and 21 MCP tools, runs completely locally, and does not require the cloud.
Rust
5.4K
4.5 points
M
Moltbrain
MoltBrain is a long-term memory layer plugin designed for OpenClaw, MoltBook, and Claude Code, capable of automatically learning and recalling project context, providing intelligent search, observation recording, analysis statistics, and persistent storage functions.
TypeScript
6.4K
4.5 points
B
Bm.md
A feature-rich Markdown typesetting tool that supports multiple style themes and platform adaptation, providing real-time editing preview, image export, and API integration capabilities
TypeScript
4.5K
5 points
S
Security Detections MCP
Security Detections MCP is a server based on the Model Context Protocol that allows LLMs to query a unified security detection rule database covering Sigma, Splunk ESCU, Elastic, and KQL formats. The latest version 3.0 is upgraded to an autonomous detection engineering platform that can automatically extract TTPs from threat intelligence, analyze coverage gaps, generate SIEM-native format detection rules, run tests, and verify. The project includes over 71 tools, 11 pre-built workflow prompts, and a knowledge graph system, supporting multiple SIEM platforms.
TypeScript
6.7K
4 points
P
Paperbanana
Python
7.9K
5 points
B
Better Icons
An MCP server and CLI tool that provides search and retrieval of over 200,000 icons, supports more than 150 icon libraries, and helps AI assistants and developers quickly obtain and use icons.
TypeScript
6.7K
4.5 points
A
Assistant Ui
assistant - ui is an open - source TypeScript/React library for quickly building production - grade AI chat interfaces, providing composable UI components, streaming responses, accessibility, etc., and supporting multiple AI backends and models.
TypeScript
7.8K
5 points
A
Apify MCP Server
The Apify MCP Server is a tool based on the Model Context Protocol (MCP) that allows AI assistants to extract data from websites such as social media, search engines, and e-commerce through thousands of ready-to-use crawlers, scrapers, and automation tools (Apify Actors). It supports OAuth and Skyfire proxy payment and can be integrated into MCP clients such as Claude and VS Code through HTTPS endpoints or local stdio.
TypeScript
6.7K
5 points
G
Gitlab MCP Server
Certified
The GitLab MCP server is a project based on the Model Context Protocol that provides a comprehensive toolset for interacting with GitLab accounts, including code review, merge request management, CI/CD configuration, and other functions.
TypeScript
25.1K
4.3 points
M
Markdownify MCP
Markdownify is a multi-functional file conversion service that supports converting multiple formats such as PDFs, images, audio, and web page content into Markdown format.
TypeScript
36.1K
5 points
D
Duckduckgo MCP Server
Certified
The DuckDuckGo Search MCP Server provides web search and content scraping services for LLMs such as Claude.
Python
73.9K
4.3 points
N
Notion Api MCP
Certified
A Python-based MCP Server that provides advanced to-do list management and content organization functions through the Notion API, enabling seamless integration between AI models and Notion.
Python
21.8K
4.5 points
F
Figma Context MCP
Framelink Figma MCP Server is a server that provides access to Figma design data for AI programming tools (such as Cursor). By simplifying the Figma API response, it helps AI more accurately achieve one - click conversion from design to code.
TypeScript
65.8K
4.5 points
U
Unity
Certified
UnityMCP is a Unity editor plugin that implements the Model Context Protocol (MCP), providing seamless integration between Unity and AI assistants, including real - time state monitoring, remote command execution, and log functions.
C#
33.1K
5 points
G
Gmail MCP Server
A Gmail automatic authentication MCP server designed for Claude Desktop, supporting Gmail management through natural language interaction, including complete functions such as sending emails, label management, and batch operations.
TypeScript
22.3K
4.5 points
M
Minimax MCP Server
The MiniMax Model Context Protocol (MCP) is an official server that supports interaction with powerful text-to-speech, video/image generation APIs, and is suitable for various client tools such as Claude Desktop and Cursor.
Python
50.3K
4.8 points
AIBase
Zhiqi Future, Your AI Solution Think Tank
English简体中文繁體中文にほんご
© 2026AIBase