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

Acesta este un SDK și un framework pentru implementarea Model Context Protocol în Go. Oferă un sistem standardizat pentru construirea de servere și clienți care schimbă resurse externe, date proprietare și instrumente executabile pentru a oferi context modelelor de limbaj mari (LLM). SDK-ul include o bibliotecă de comunicare JSON-RPC și un framework de integrare pentru a expune date locale, șabloane de prompt-uri și funcții tipizate către modelele AI. Permite dezvoltarea atât a serverelor de protocol care oferă context extern, cât și a clienților care consumă aceste instrumente și resurse remote. Proiectul acoperă gestionarea ciclului de viață al conexiunii și negocierea versiunii de protocol pentru a asigura interoperabilitatea. Oferă abstracții de transport pentru schimbul de mesaje prin input/output standard sau HTTP, alături de capabilități pentru maparea resurselor și gestionarea sesiunilor. Funcțiile de securitate și observabilitate includ integrarea identității OAuth, restricții de acces la directoare pentru servere și instrumente pentru inspecția traficului și verificarea capabilităților.

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