# prom-mcp (mcpb · prom-mcp_v0.2.0_linux_amd64.mcpb)

Prometheus MCP server for AI agents: PromQL queries, alert explanations, series discovery.

- Trust score: 34/100 (low)
- Change this week: +25
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-10-19

## Components

- mcpb · `prom-mcp_v0.2.0_darwin_amd64.mcpb`: 9/100, [markdown](https://verifymcp.io/servers/aniketatgithub-prom-mcp/prom-mcp-darwin-amd64.md), [page](https://verifymcp.io/servers/aniketatgithub-prom-mcp/prom-mcp-darwin-amd64)
- mcpb · `prom-mcp_v0.2.0_darwin_arm64.mcpb`: 9/100, [markdown](https://verifymcp.io/servers/aniketatgithub-prom-mcp/prom-mcp-darwin-arm64.md), [page](https://verifymcp.io/servers/aniketatgithub-prom-mcp/prom-mcp-darwin-arm64)
- mcpb · `prom-mcp_v0.2.0_linux_amd64.mcpb`: 34/100 (this document), [markdown](https://verifymcp.io/servers/aniketatgithub-prom-mcp/prom-mcp-linux-amd64.md), [page](https://verifymcp.io/servers/aniketatgithub-prom-mcp/prom-mcp-linux-amd64)
- mcpb · `prom-mcp_v0.2.0_linux_arm64.mcpb`: 9/100, [markdown](https://verifymcp.io/servers/aniketatgithub-prom-mcp/prom-mcp-linux-arm64.md), [page](https://verifymcp.io/servers/aniketatgithub-prom-mcp/prom-mcp-linux-arm64)

## Channel facts

- Registry: `mcpb`
- Package: `https://github.com/aniketatgithub/prom-mcp/releases/download/v0.2.0/prom-mcp_v0.2.0_linux_amd64.mcpb`
- Version: `0.2.0`
- Transport: `stdio`
- SHA-256: `6404193ab4c2d3a61ccaf9f13d6d00a6ea4a1b15c64e0d0803fbc6fd0f58985c`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-10-19.

- **Supply Chain Security**: 0/100
  - No malware scan is available for this kind of package: the supply-chain vendors we use do not cover it. This is a permanent gap in our coverage, not a finding about the package.
  - Known CVEs could not be checked: we found no dependency list to check in this bundle: either it runs a compiled binary, whose bundled manifests we do not read, or none of the package.json, requirements.txt, pyproject.toml or uv.lock files it ships for its runtime declares a dependency we can read (a requirements.txt that only points at a setup.py project, a local path, or a nested requirements file we cannot follow counts as none).
  - Install-script risk not yet assessed.
  - Dependency health could not be checked: we found no dependency list to check in this bundle: either it runs a compiled binary, whose bundled manifests we do not read, or none of the package.json, requirements.txt, pyproject.toml or uv.lock files it ships for its runtime declares a dependency we can read (a requirements.txt that only points at a setup.py project, a local path, or a nested requirements file we cannot follow counts as none).
- **Provenance & Transparency**: 45/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 9 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 56/100
  - 33% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (good).
  - Tool/resource definitions use about 288 tokens (~41/item across 7 items; 5 tools + 2 resources), lean.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 0/100
  - Stability not yet verified: not enough scan history yet (needs a 30-day window).
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 5 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 6 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 20/100
  - Spec-recency check failed: implements MCP spec 2024-11-05; the latest is 2026-07-28.

**Unverified: 2 categories.** Categories scored 0 because we could not verify them: a data source with nothing on this package, evidence we could not reach, or a check we could not run. We only credit what we can confirm.

## Install

### How do I install the prom-mcp server?

prom-mcp ships as an MCPB bundle, a single file you download and open in an MCP host that supports MCPB bundles, such as Claude Desktop. The host reads the launch command from the bundle's own manifest, so there is no command to copy. Before opening it, check the file's SHA-256 matches the one the MCP registry declares for it, 6404193ab4c2d3a61ccaf9f13d6d00a6ea4a1b15c64e0d0803fbc6fd0f58985c; the commands to do that are on this page. If it does not match, do not open it.

- Download bundle: `https://github.com/aniketatgithub/prom-mcp/releases/download/v0.2.0/prom-mcp_v0.2.0_linux_amd64.mcpb`
- SHA-256: `6404193ab4c2d3a61ccaf9f13d6d00a6ea4a1b15c64e0d0803fbc6fd0f58985c`

### Verify on macOS / Linux

```bash
echo "6404193ab4c2d3a61ccaf9f13d6d00a6ea4a1b15c64e0d0803fbc6fd0f58985c  prom-mcp_v0.2.0_linux_amd64.mcpb" | shasum -a 256 -c
```

### Verify on Windows (PowerShell)

```powershell
(Get-FileHash -Algorithm SHA256 .\prom-mcp_v0.2.0_linux_amd64.mcpb).Hash -eq "6404193ab4c2d3a61ccaf9f13d6d00a6ea4a1b15c64e0d0803fbc6fd0f58985c"
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-10-14 (score 34, +25)

- [security regression] Provenance: pass → unverified
- [security improvement] Injection markers: unverified → pass
- [security] First check of Judged manipulation: pass
- [security] Stability: Stability not yet verified: not enough scan history yet (needs a 30-day window).
- [functional regression] MCP protocol: unverified → fail
- [functional improvement] Tool coverage: unverified → 100
- [functional improvement] Schema quality: unverified → 33
- [functional] First check of Schema quality: fail
- [functional] First check of Destructive annotations: pass
- [functional] First check of Schema quality: pass
- [functional] First check of Schema quality: good
- [functional] First check of Tool coverage: 100

### 2026-10-11 (score 9)

First indexed and scored.

## MCP tools (5)

### `prom_query` (~47 tokens)

Run an instant PromQL query against Prometheus and return the series and values.

Input parameters:

- `query` (string, required): PromQL expression, e.g. up or rate(http_requests_total[5m])

### `prom_alerts_explain` (~29 tokens)

List active Prometheus alerts joined with their rule expressions and annotations, explained for triage.

### `prom_query_range` (~79 tokens)

Run a range PromQL query over the last N minutes and summarize each series (points, first, last, min, max).

Input parameters:

- `minutes` (number): Lookback window in minutes (default 60)
- `query` (string, required): PromQL expression
- `step` (string): Step like 60s or 5m (optional)

### `prom_label_values` (~63 tokens)

List the values of one label, optionally scoped by a series matcher. Use to learn real job/instance names before querying.

Input parameters:

- `label` (string, required): Label name, e.g. job
- `match` (string): Optional series selector, e.g. up

### `prom_series_discover` (~56 tokens)

Discover which series exist for a label matcher, e.g. up{job="node"}, before writing queries against them.

Input parameters:

- `match` (string, required): Series selector, e.g. up or http_requests_total{job="api"}

## Diagnostics

Captured diagnostic sections: Provenance. The full working is on the page: https://verifymcp.io/servers/aniketatgithub-prom-mcp/prom-mcp-linux-amd64#diagnostics

## Score history

- 2026-10-19: 34
- 2026-10-18: 34
- 2026-10-17: 34
- 2026-10-16: 34
- 2026-10-15: 34
- 2026-10-14: 34
- 2026-10-13: 9
- 2026-10-12: 9
- 2026-10-11: 9

## Common questions

### What is the prom-mcp server?

prom-mcp is listed in the public MCP registry as io.github.aniketatgithub/prom-mcp. Prometheus MCP server for AI agents: PromQL queries, alert explanations, series discovery. This page covers its MCPB bundle (https://github.com/aniketatgithub/prom-mcp/releases/download/v0.2.0/prom-mcp_v0.2.0_linux_amd64.mcpb).

### Is the prom-mcp server safe to use?

prom-mcp scores 34 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

### What tools does the prom-mcp server expose?

prom-mcp exposes 5 tools: prom_query, prom_alerts_explain, prom_query_range, prom_label_values, prom_series_discover. Their descriptions and schemas cost roughly 274 tokens of context every time the server is loaded.

### What licence is the prom-mcp server under?

prom-mcp declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.

## Links

- Repository: https://github.com/aniketatgithub/prom-mcp
- Changelog RSS feed: https://verifymcp.io/servers/aniketatgithub-prom-mcp/prom-mcp-linux-amd64.xml
- Changelog JSON feed: https://verifymcp.io/servers/aniketatgithub-prom-mcp/prom-mcp-linux-amd64.json
- HTML version of this page: https://verifymcp.io/servers/aniketatgithub-prom-mcp/prom-mcp-linux-amd64