Neo4j Knowledge Graph MCP Server
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AlicenseAqualityAmaintenanceProvides long-term memory and a temporal knowledge graph for AI agents, enabling persistent memory and reasoning across sessions.331MIT- AlicenseNot gradedqualityCmaintenanceProvides persistent knowledge graph memory for AI agents, enabling them to store, recall, and query facts about people, projects, and relationships across sessions.MIT

Memsolus MCP Serverofficial
AlicenseAqualityDmaintenanceProvides persistent long-term memory for AI agents through semantic search and automated knowledge graph extraction. It enables agents to store, recall, and reason over facts, preferences, and relationships across multiple conversations and sessions.1412 npmMIT- AlicenseBqualityDmaintenanceScalable, high-performance knowledge graph memory system with semantic search, temporal awareness, and advanced relation management.1725 npm424MIT
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TDQS
Scored across 21 tools
Each tool targets a distinct operation on entities, observations, or relations. Batch variants are clearly differentiated, and query tools like search_nodes and semantic_search serve different purposes without ambiguity.
All tool names follow a consistent verb_noun pattern (e.g., create_entities, delete_observations, get_relation_history). The '_batch' suffix is uniformly applied for optimized versions, and naming conventions are predictable throughout.
With 21 tools covering CRUD, batch operations, time-travel queries, semantic search, and embeddings, the count is well-scoped for a knowledge graph server. Each tool serves a clear purpose without excess.
The tool surface covers core entity, relation, and observation lifecycle management. However, there is no explicit update_observations tool, and batch update for relations is limited to a single update_relation. These are minor gaps in an otherwise comprehensive set.