MCPs tagged with knowledge-graph
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MemoryMesh
A knowledge graph server for structured AI memory and context management.
MemoryMesh is a knowledge graph server designed to help AI models maintain structured, consistent memory, especially for interactive storytelling and RPG contexts. It is based on the Model Context Protocol (MCP), as explicitly stated, and retains core MCP server functionalities. By utilizing dynamic, schema-based configuration, the server enables creation and management of nodes and relationships, offering comprehensive tools for data integrity, feedback, and event tracking. MemoryMesh emphasizes flexibility, supporting both predefined and dynamic schemas for guiding AI interactions.
- ⭐ 313
- MCP
- CheMiguel23/MemoryMesh
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ApeRAG
Hybrid RAG platform with MCP integration for intelligent knowledge management
ApeRAG is a production-ready Retrieval-Augmented Generation (RAG) platform that integrates graph-based, vector, and full-text search capabilities. It enables the construction of knowledge graphs and supports MCP (Model Context Protocol), allowing AI assistants direct interaction with knowledge bases. Features include advanced document parsing, multimodal processing, intelligent agent workflows, and enterprise management tools. Deployment is streamlined via Docker and Kubernetes, with extensive support for customization and scalability.
- ⭐ 920
- MCP
- apecloud/ApeRAG
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Neo4j MCP Clients & Servers
Seamless natural language and knowledge graph integration for Neo4j via Model Context Protocol.
Neo4j MCP Clients & Servers provide standardized interfaces that enable large language models and AI assistants to interact with Neo4j databases and cloud services using natural language through the Model Context Protocol (MCP). It includes multiple servers for translating natural language to Cypher queries, managing graph memory, handling Neo4j Aura cloud services, and supporting interactive data modeling. Multiple transport modes such as STDIO, HTTP, and SSE offer flexibility for various deployments including cloud and local environments.
- ⭐ 797
- MCP
- neo4j-contrib/mcp-neo4j
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Agent-MCP
Multi-Agent Collaboration Protocol for Coordinated AI Software Development
Agent-MCP enables advanced multi-agent orchestration for AI-driven software development, allowing specialized agents to collaborate via a shared persistent context. Its system manages parallel execution, persistent memory graphs, and real-time task coordination with robust visualization tools. Designed for experienced developers, it features intelligent task management and conflict prevention for scalable and complex codebases. The protocol emphasizes maintaining and sharing context among agents through standardized mechanisms.
- ⭐ 1,021
- MCP
- rinadelph/Agent-MCP