Prometheus MCP Server

Name: Prometheus MCP Server
Author: prometheus

by prometheusversion 0.19.0-rc.0i★ –iRuns on your computer

Registry nameio.github.prometheus/prometheus-mcp

Install

Overview

Description from the maker (original English)

An API-complete MCP server to manage Prometheus-compatible backends.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

DockerDocker installed

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "prometheus": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "docker.io/prom/prometheus-mcp:v0.19.0-rc.0"
      ]
    }
  }
}

Run this in your terminal

claude mcp add prometheus -- docker run -i --rm docker.io/prom/prometheus-mcp:v0.19.0-rc.0

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "prometheus": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "docker.io/prom/prometheus-mcp:v0.19.0-rc.0"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "prometheus": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "docker.io/prom/prometheus-mcp:v0.19.0-rc.0"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account prometheus, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32476 servers in this category
Published to the registry more recently than 88% of the other 32476 servers in this category
Basic checks equal to or better than 100% of the other 32476 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Prometheus MCP Server safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.19.0-rc.0) reached the registry 4 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, zanadir

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated yesterday

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Prometheus MCP Server

About

This is an MCP server to allow LLMs to interact with a running Prometheus instance via the API to do things like generate and execute promql queries, list and analyze metrics, etc.

Demos and Examples

Asking Claude to Investigate Slow Queries

The prompt used was:

querying my metrics is slow, can you help me figure out why?

Investigate metrics produced by the MCP server itself and suggest recording rules for SLOs

The prompt used was:

use the tools from the prometheus mcp server to investigate the metrics from

the mcp server and suggest prometheus recording rules for SLOs

Summarize Prometheus metric/label naming best practices

The prompt used was:

summarize prometheus metric/label name best practices

Report on the health of the Prometheus instance that powers prometheus.demo.prometheus.io

The prompt used was:

please provide a comprehensive review and summary of the prometheus server.

review it's configuration, flags, runtime/build info, and anything else that

you feel may provide insight into the status of the prometheus instance,

including analyzing metrics and executing queries

Tools

The Prometheus HTTP API outputs JSON data, and the tools in this MCP server return that JSON to the LLM for processing as it's structured and well understood by LLMs.

LLMs and Token/Context Efficiency

This MCP server supports the following options which have the potential to reduce token/context usage:

TOON Encoding

If token/context usage is a concern, this MCP server also supports converting the API's JSON data to the Token-Oriented Object Notation (TOON) format. While it is not guaranteed to reduce token usage, it is designed with token efficiency in mind. As noted on TOON's documentation, it excels at uniform arrays of objects; non-uniform/complex objects may still be more token-efficient in JSON. Real world token usage will depend on usage patterns, please review common workflows to determine if TOON output may be beneficial. Please see Flags for more information on the available flags and their corresponding environment variables.

API Response Truncation

This feature allows you to set a maximum limit on the number of lines or entries returned from the Prometheus API for, which can help in reducing the amount of data sent to the LLM. Setting the limit to 0 disables truncation. Truncation is disabled by default. Note that LLMs capable of handling tool request arguments can override this global truncation limit on a per-tool-call basis for supported tools. Please see Flags for more information on the available flags and their corresponding environment variables.

Full Tool List

Tool Name	Description
`alertmanagers`	Get overview of Prometheus Alertmanager discovery
`build_info`	Get Prometheus build information
`config`	Get Prometheus configuration
`docs_list`	List of Official Prometheus Documentation Files
`docs_read`	Read the named markdown file containing official Prometheus documentation from the prometheus/docs repo
`docs_search`	Search the markdown files containing official Prometheus documentation from the prometheus/docs repo
`exemplar_query`	Performs a query for exemplars by the given query and time range
`flags`	Get runtime flags
`healthy`	Management API endpoint that can be used to check Prometheus health
`label_names`	Returns the unique label names present in the block in sorted order by given time range and matchers
`label_values`	Performs a query for the values of the given label, time range and matchers
`list_alerts`	List all active alerts
`list_rules`	List all alerting and recording rules that are loaded
`list_targets`	Get overview of Prometheus target discovery
`metric_metadata`	Returns metadata about metrics currently scraped by the metric name
`query`	Execute an instant query against the Prometheus datasource, returning one value per series at a single point in time
`quit`	Management API endpoint that can be used to trigger a graceful shutdown of Prometheus
`range_query`	Execute a range query against the Prometheus datasource, returning values over a time window
`ready`	Management API endpoint that can be used to check Prometheus is ready to serve traffic (i.e. respond to queries
`reload`	Management API endpoint that can be used to trigger a reload of the Prometheus configuration and rule files
`runbooks_list`	List the runbooks embedded in this server: guided workflows (Agent Skills) for common Prometheus tasks
`runbooks_read`	Read the named runbook by skill name (e.g. `check-system-health`)
`runtime_info`	Get Prometheus runtime information
`series`	Finds series by label matchers
`targets_metadata`	Returns metadata about metrics currently scraped by the target
`tsdb_stats`	Get usage and cardinality statistics from the TSDB
`wal_replay_status`	Get current WAL replay status

NOTE:

Because the TSDB Admin API endpoints

allow for potentially destructive operations like deleting data, they are not

enabled by default. In order to enable the TSDB Admin API endpoints, the MCP

server must be started with the flag --dangerous.enable-tsdb-admin-tools to

acknowledge the associated risk these endpoints carry.

Tool Name	Description
`clean_tombstones`	Removes the deleted data from disk and cleans up the existing tombstones
`delete_series`	deletes data for a selection of series in a time range
`snapshot`	creates a snapshot of all current data into snapshots/ - under the TSDB's data directory and returns the directory as response

Tool Sets

The server exposes many tools to interact with Prometheus. There are tools to interact with Prometheus via the API, as well as additional tools to do things like read documentation, etc. By default, they are all registered and available for use (TSDB Admin API tools need an extra flag).

To be considerate to LLMs with smaller context windows, it's possible to pass in a whitelist of specific tools to register with the server. The following 'core' tools are always loaded: [docs_list, docs_read, docs_search, runbooks_list, runbooks_read, query, range_query, metric_metadata, label_names, label_values, series]. Additional tools can be specified with the --mcp.tools flag.

For example, the command line:

prometheus-mcp --mcp.tools=build_info --mcp.tools=flags --mcp.tools=runtime_info

Would result in the following tools being loaded:

build_info
docs_list
docs_read
docs_search
flags
label_names
label_values
metric_metadata
query
range_query
runbooks_list
runbooks_read
runtime_info
series

Runbooks (Agent Skills) and Prompts

The server embeds a set of runbooks: guided workflows for common Prometheus tasks, expressed in terms of the server's tools. Each runbook orients the model on the relevant tools, then suggests topics to explore with example queries rather than prescribing a fixed sequence of steps. Runbooks cover tasks like system health checks, missing-data triage, error-rate investigation, high-cardinality optimization, recording/alerting rule review, and configuration/performance tuning.

Each runbook is packaged as a full Agent Skill: a directory containing a SKILL.md with name/description frontmatter. Runbooks are exposed three ways:

Tools: the model can discover and read them itself via the runbooks_list/runbooks_read tools when a request matches a runbook's purpose. Tools are the most portable path and work in every MCP client.
Skill resources: per the SEP-2640 skills extension draft, each runbook is a skill:// /SKILL.md resource, enumerated by the well-known skill://index.json discovery index, and the server declares the io.modelcontextprotocol/skills extension capability. Skill-aware hosts can consume these like local filesystem skills; other clients can still read them as ordinary MCP resources.
MCP prompts: each runbook is also registered as an MCP prompt under its skill name (e.g. check-system-health, optimize-high-cardinality), so clients with prompt support can invoke a guided workflow directly (often surfaced as slash commands).

Prometheus Compatible Backends

There are many Prometheus compatible backends that can be used to extend prometheus in a variety of ways, often with the goals of offering long term storage or query aggregation from multiple prometheus instances. Some examples can be found in the Remote Storage of prometheus' docs.

Many of those services also offer a "prometheus compatible" API that can be used to query/interact with the data using native promQL. In general, this MCP server should at a minimum work for other prometheus API compatible services to execute queries and interact with the series/labels/metadata endpoints for metric and label discovery. Beyond that, there may be API differences as the different systems implement different parts/extensions of the API for their needs.

Examples:

Thanos does not use a centralized config, so the config endpoint is not implemented and thus the config tool is removed.
Mimir and Cortex implement extra endpoints to manage/add/remove rules

To workaround this and provide a better experience on some of the commonly used Prometheus compatible systems, this project may add direct support for select systems to provide different/more tools. Choosing a specific prometheus backend implementation can be done with the --prometheus.backend flag. The list of available backend implementations on a given release of the MCP server can be found in the output of the --help flag. Qualifications and support criteria are still under consideration, please open an issue to request support/features for a specific backend for further discussion.

Prometheus Backend Implementation Differences

| Backend |

Only the beginning is shown; read the rest in the repo

Prometheus MCP Server

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

zanadir

README from the maker

Prometheus MCP Server

About

Demos and Examples

Asking Claude to Investigate Slow Queries

Investigate metrics produced by the MCP server itself and suggest recording rules for SLOs

Summarize Prometheus metric/label naming best practices

Report on the health of the Prometheus instance that powers prometheus.demo.prometheus.io

Tools

LLMs and Token/Context Efficiency

TOON Encoding

API Response Truncation

Full Tool List

Tool Sets

Runbooks (Agent Skills) and Prompts

Prometheus Compatible Backends

Prometheus Backend Implementation Differences