Awesome GitHub RepositoriesModel Context Protocol Servers

Codegraph is a local codebase indexer and static analysis graph database that serves as a context provider for AI agents. It parses multiple programming languages into a searchable knowledge graph of symbols and dependencies, exposing these relationships to AI tools through the Model Context Protocol. The project distinguishes itself by aggregating relevant code snippets and symbol flows to reduce token usage for large language models. It automates the configuration of server settings and steering instructions across various AI agent platforms and command line editors to enable automatic code

Graphiti is a backend framework and memory server designed to provide artificial intelligence agents with persistent, time-aware knowledge graph storage. It functions as a memory layer that enables agents to maintain context across long-term interactions by recording and evolving structured data over time. The system distinguishes itself through a specialized temporal graph database that tracks how entities and relationships change using validity windows. By combining semantic vector similarity, keyword matching, and graph topology traversal, the engine performs hybrid retrieval to locate rel

Unstract is an unstructured data extraction system and ETL pipeline orchestrator that uses large language models to convert documents, images, and scans into structured JSON. It provides a document extraction API for integrating these capabilities into external automation tools and includes a Model Context Protocol server to connect AI agents to structured information retrieval. The system ensures data accuracy through a verification tool featuring dual-model verification and human-in-the-loop review with coordinate-based document highlighting. It utilizes natural language extraction schemas

Materialize is a streaming SQL database that continuously ingests live data from sources such as Kafka, Redpanda, PostgreSQL, and MySQL, and incrementally maintains materialized views. It provides a PostgreSQL-compatible query engine that accepts standard SQL over the PostgreSQL wire protocol, enabling any existing SQL client or BI tool to query real-time data. The system also includes a Model Context Protocol (MCP) server that exposes live materialized view data to AI agents, providing fresh context without polling. Materialize distinguishes itself through its ability to offer configurable c

ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself

这是一个用于在 Go 中实现 Model Context Protocol 的软件开发工具包（SDK）和框架。它提供了一套标准化的系统，用于构建交换外部资源、专有数据和可执行工具的服务器与客户端，从而为大语言模型提供上下文。 该 SDK 包含一个 JSON-RPC 通信库和一个集成框架，用于向 AI 模型公开本地数据、提示词模板和类型化函数。它支持开发提供外部上下文的协议服务器，以及消费这些远程工具和资源的客户端。 该项目涵盖了连接生命周期管理和协议版本协商，以确保互操作性。它提供了通过标准输入/输出或 HTTP 进行消息交换的传输抽象，以及资源映射和会话管理功能。 安全和可观测性功能包括 OAuth 身份集成、服务器目录访问限制，以及用于流量检查和能力验证的工具。

This project is a Model Context Protocol server that bridges messaging platforms with AI assistants. It functions as middleware to facilitate the secure exchange of chat data, enabling external AI agents to access, search, and analyze historical conversation logs through a standardized interface. The server distinguishes itself by automating the ingestion and archiving of messaging streams into a local relational database. It supports secure, non-manual session authentication using QR codes, allowing for persistent data collection without continuous human oversight. Once archived, the system