MCP Servers

query-layer

Exposes a metrics query API to AI assistants, allowing them to issue queries and interact with the server's tools such as ping.

Category

README

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.

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.

Recommended Servers

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

E2B

Using MCP to run code via e2b.

Neon Database

MCP server for interacting with Neon Management API and databases

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.