6 Repos
Servers that expose structured data and graph entities to AI agents via the Model Context Protocol.
Distinct from Graph Data Models: Focuses on the MCP protocol implementation for graph data exposure, distinct from general graph data models.
Explore 6 awesome GitHub repositories matching data & databases · Model Context Protocol Servers. Refine with filters or upvote what's useful.
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
Acts as an MCP server that exposes a structured graph of code flows and symbols to AI agents.
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
Exposes graph entities and relationships via the Model Context Protocol for real-time agent access.
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
Implements a Model Context Protocol server that allows AI agents to process documents and receive structured results.
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
Ships an MCP server that exposes live materialized view data to AI agents for real-time context.
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
Provides a Model Context Protocol server that allows AI assistants to query and manage machine learning executions and project context.
Dies ist ein Software Development Kit (SDK) und Framework zur Implementierung des Model Context Protocol in Go. Es bietet ein standardisiertes System zum Aufbau von Servern und Clients, die externe Ressourcen, proprietäre Daten und ausführbare Tools austauschen, um Large Language Models (LLMs) Kontext bereitzustellen. Das SDK enthält eine JSON-RPC-Kommunikationsbibliothek und ein Integrations-Framework, um lokale Daten, Prompt-Templates und typisierte Funktionen für KI-Modelle bereitzustellen. Es ermöglicht die Entwicklung von Protokoll-Servern, die externen Kontext liefern, sowie von Clients, die diese Remote-Tools und Ressourcen nutzen. Das Projekt deckt das Connection-Lifecycle-Management und die Protokoll-Versionsaushandlung ab, um Interoperabilität zu gewährleisten. Es bietet Transport-Abstraktionen für den Nachrichtenaustausch via Standard-Input/Output oder HTTP sowie Funktionen für Resource-Mapping und Session-Management. Sicherheits- und Observability-Features umfassen OAuth-Identitätsintegration, Verzeichniszugriffsbeschränkungen für Server sowie Tools zur Traffic-Inspektion und Capability-Verifizierung.
Enables providing local data and tools to AI models through a standardized communication protocol.