raihan0824-prometheus-mcp-server

Provides a Model Context Protocol (MCP) server that lets AI assistants connect to a Prometheus server, discover metrics/targets, and run PromQL instant and range queries via MCP tools. Configuration is primarily done through environment variables (PROMETHEUS_URL and optional credentials/tokens).

Evaluated Apr 04, 2026 (16d ago)
Homepage ↗ Repo ↗ Monitoring ai-ml monitoring prometheus mcp observability metrics devtools
⚙ Agent Friendliness
60
/ 100
Can an agent use this?
🔒 Security
54
/ 100
Is it safe for agents?
⚡ Reliability
32
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
75
Documentation
70
Error Messages
0
Auth Simplicity
80
Rate Limits
10

🔒 Security

TLS Enforcement
80
Auth Strength
60
Scope Granularity
20
Dep. Hygiene
55
Secret Handling
55

TLS enforcement is not explicitly documented for the MCP server or upstream connections; assume HTTPS is supported but not guaranteed. Authentication covers upstream Prometheus using basic credentials or bearer token from env vars, but there is no described fine-grained scoping or per-user authorization for MCP clients. Secrets are configured via environment variables (better than hardcoding), but README does not state whether secrets are redacted from logs/telemetry. Dependency hygiene is unknown from provided data; a moderate score reflects the presence of common libraries but no CVE/audit information.

⚡ Reliability

Uptime/SLA
0
Version Stability
55
Breaking Changes
30
Error Recovery
45
AF Security Reliability

Best When

You want an MCP-enabled AI assistant to query Prometheus (including metric discovery) from a controlled environment where Prometheus access is already secured.

Avoid When

You need fine-grained authorization for different metrics/users or you require clearly documented rate-limit and retry/idempotency semantics from this MCP server.

Use Cases

  • Chat/agent-based querying and analysis of Prometheus metrics using standardized MCP tooling
  • Automating investigative workflows (e.g., identify relevant metrics, inspect metadata, run instant/range queries)
  • Building internal AI assistants for monitoring and troubleshooting
  • Exploring metrics and targets for dashboards/alerting setup

Not For

  • Exposing Prometheus data publicly to untrusted users without strong network/auth controls
  • High-throughput or cost-sensitive workloads without verifying rate limiting and query limits on the Prometheus side
  • Use as a general-purpose Prometheus API; it focuses on MCP tool endpoints for metrics operations

Interface

REST API
No
GraphQL
No
gRPC
No
MCP Server
Yes
SDK
No
Webhooks
No

Authentication

Methods: Basic auth via PROMETHEUS_USERNAME and PROMETHEUS_PASSWORD Bearer token auth via PROMETHEUS_TOKEN
OAuth: No Scopes: No

Authentication described is for the upstream Prometheus connection (not for authenticating callers of the MCP server).

Pricing

Free tier: No
Requires CC: No

Open-source (MIT) as indicated by manifest/README; pricing not applicable.

Agent Metadata

Pagination
none
Idempotent
False
Retry Guidance
Not documented

Known Gotchas

  • Agent-driven PromQL queries can be expensive; this README does not document query cost controls or recommended guardrails—coordinate with your Prometheus limits.
  • Rate limits are not described for the MCP server interface; if rate-limited by Prometheus/network, agents may need conservative query pacing.
  • No evidence in README of MCP-server-side auth for MCP clients; treat MCP server as trusted/internal only.

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Scores are editorial opinions as of 2026-04-04.

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