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query-layer

by ramraj-patel
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Python

MCP Server for Query Layer API

An MCP (Model Context Protocol) server that exposes a metrics query-layer API to AI assistants. Built with the official Python MCP SDK v2.

Quick Start

# Prerequisites: Python 3.10+, uv
uv sync
uv run mcp dev src/server.py

Open the MCP Inspector at the URL printed to the console. You'll see the ping tool available.

Related MCP server: prometheus-mcp

Cursor Integration

The .cursor/mcp.json file is pre-configured. Restart Cursor and the query-layer MCP server will be available to the AI assistant.

Local Tracing with Phoenix (Optional)

# Terminal 1: start Phoenix
uv run phoenix serve

# Terminal 2: run the server pointing at Phoenix
export PHOENIX_COLLECTOR_ENDPOINT=http://localhost:6006
uv run mcp dev src/server.py

View traces at http://localhost:6006 — Phoenix natively renders MCP tool calls, prompts, and GenAI spans.

Documentation

Document

Purpose

Plan

Problem statement, solution approach, implementation plan

Review

Production-grade plan review

Design

MCP concepts, technology choices, design decisions

Getting Started

Setup, install, run, configure, troubleshoot

Observability

Logging, tracing, OTLP, local Phoenix

Project Structure

src/
  server.py          # MCPServer + tools (entry point)
  logging_config.py  # Structured JSON logging to stderr
  otel_config.py     # OpenTelemetry exporter configuration
.cursor/
  mcp.json           # Cursor MCP server configuration
docs/
  plan.md            # Full implementation plan
  review.md          # Plan review
  design.md          # Design document
  getting-started.md # Setup guide
  observability.md   # Observability guide

License

Apache 2.0 — see LICENSE.

This server cannot be installed

A
license - permissive license
Not graded
quality - not tested
B
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

Resources

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