Model Context Protocol (MCP)

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

9483 stars · 1877 forks observed · 2026-10-20

What it is AI-extracted, prompt v1, taxonomy v1, 2026-09-21, confidence 0.97

Reference implementations of Model Context Protocol (MCP) servers demonstrating how AI applications can securely access tools and data sources. Includes example servers for file systems, web fetching, Git, memory, and more across multiple languages.

Use cases

• connect an LLM to a local file system for reading and writing documents
• enable an AI assistant to fetch and summarize web content
• provide a Git server interface for AI-driven code analysis
• expose a persistent memory graph for long‑term context in AI agents
• allow AI models to perform time zone conversions and scheduling
• build custom MCP servers to integrate proprietary APIs with LLMs

When to choose

• You need a reference starting point for building your own MCP server
• You want multi‑language examples of how to expose tools to LLMs
• Your project requires secure, standardized access for AI agents to external resources
• You are developing an AI‑driven application that needs to call tools via a protocol

When to avoid

• You need a production‑grade, out‑of‑the‑box server with built‑in security hardening
• Your use case does not involve LLMs or AI agents
• You require a single‑language solution without cross‑language SDKs

Facets

service · maturity active

api-framework rpc cli artificial-intelligence python go rust jvm dotnet ruby php cross-platform model-context-protocol reference-implementation mcp tool-integration secure-llm-access ai-agents agent-framework nodejs swift typescript

Member repositories

For agents

markdown · JSON · MCP: product_card(name="modelcontextprotocol/modelcontextprotocol")