mcp-graphql and github_graphql_api_mcp

Both projects are Model Context Protocol (MCP) servers, with one (blurrah/mcp-graphql) providing a general implementation for GraphQL and the other (wanzunz/github_graphql_api_mcp) being a specific application of an MCP server tailored for the GitHub GraphQL API, making them ecosystem siblings in the context of MCP and GraphQL.

mcp-graphql
57
Established
github_graphql_api_mcp
28
Experimental
Maintenance 2/25
Adoption 10/25
Maturity 25/25
Community 20/25
Maintenance 6/25
Adoption 3/25
Maturity 7/25
Community 12/25
Stars: 365
Forks: 59
Downloads:
Commits (30d): 0
Language: TypeScript
License: MIT
Stars: 4
Forks: 1
Downloads:
Commits (30d): 0
Language: Python
License:
Stale 6m
No License No Package No Dependents

About mcp-graphql

blurrah/mcp-graphql

Model Context Protocol server for GraphQL

This is a server that lets Large Language Models (LLMs) connect to and use GraphQL APIs. It takes a GraphQL endpoint (and optionally headers or a schema file) and outputs a way for an LLM to understand and query that API dynamically. Developers building LLM-powered applications will use this to enable their models to interact with existing GraphQL services.

LLM-development API-integration AI-application-development GraphQL-services

About github_graphql_api_mcp

wanzunz/github_graphql_api_mcp

A Model Control Protocol (MCP) server for exploring the GitHub GraphQL schema and executing optimized queries. Provides AI assistants with efficient GitHub data retrieval capabilities through GraphQL.

Implements schema introspection tools that enable AI assistants to dynamically discover GitHub GraphQL capabilities without predefined tool definitions, while executing single optimized queries that fetch multiple related resources in one request rather than chaining sequential API calls. Built as an MCP server using Python with stdio transport, it reduces token consumption by allowing precise field selection through GraphQL instead of receiving full REST API responses. Integrates directly with Claude and other MCP-compatible AI clients, positioning itself as a lightweight alternative to REST-based GitHub integrations that require frequent round-trips and context-heavy intermediate outputs.

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