$ loading_
Connect a Neo4j knowledge graph to AI assistants for search and read-only queries.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "NeuroSpace MCP Server" yet — see the docs or source repo.
Use NeuroSpace MCP Server to search for the entity "GraphRAG" and list its direct neighboring nodes and relationship types.
A matched entity result plus its directly connected nodes and relationship descriptions.
Through NeuroSpace MCP Server, run a read-only Cypher query to count nodes by label in the knowledge graph and present the results in a table.
A table summarizing counts by node label, without modifying graph data.
Using NeuroSpace MCP Server, start from the entity "Neo4j" and traverse neighbors to find key related entities within two hops, organized by relationship path.
Key entities within two hops and their relationship paths for quick graph understanding.
Developers or researchers can expose a Neo4j knowledge graph to an AI assistant via MCP and use natural language for entity search, neighbor traversal, and graph queries.
Data analysts can use read-only Cypher queries to inspect entity distribution, relationships, and local graph structure without changing underlying data.
Researchers can traverse neighboring nodes around an entity to inspect related clues and contextual relationships, improving knowledge discovery efficiency.
It is an MCP tool that exposes a Neo4j knowledge graph to AI assistants. Based on the description, it supports entity search, neighbor traversal, and read-only Cypher queries.
From the description, it provides read-only Cypher query capability. That means the stated functionality does not include writing or modifying graph data.
At minimum, you need a Neo4j knowledge graph and an AI assistant environment that supports MCP. For installation and configuration details, see the source repository.
Run Cypher queries and inspect Neo4j schemas for AI-driven graph exploration.
Ingest documents into Neo4j to build and query a knowledge graph.
Query and explore Neo4j graph databases with natural language and Cypher.
Give LLMs persistent knowledge graph memory with semantic retrieval and contextual recall.
Turn PostgreSQL data into a Neo4j graph for natural language querying.
Adds nodes, edges, and semantic retrieval to agent knowledge graphs.