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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 deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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