LitCoin MCP Server - Semantic + Structural Knowledge Graph query for AI agents
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# LitCoin MCP Server MCP server for the LitCoin Knowledge Graph (KG) - query LitCoin knowledge graph for nodes, edges, relationships as well as semantic similarity search over nodes and relationships. It supports semantic + structural LitCoin knowledge graph access for AI agents via MCP. ## This MCP server exposes the following tools: - get_node(node_id: CURIE str) - Get information about a specific node by CURIE. - get_node_edges(node_id: CURIE str) - Get all edges connected to a node. - get_edges_between(source_id: CURIE str, target_id: CURIE str) - Find all edges connecting two nodes by CURIEs - get_semantic_similar_nodes(query: str, top_k: int = 10, k_per_index: int = 2) - Find top_k semantically similar nodes to an input text query - get_semantic_similar_edges(query: str, top_k: int = 10, k_per_index: int = 2) - Find top_k semantically similar relationships to an input text query Note that for semantic search tools to work, this MCP server assumes Neo4j embedding vector indexes already exist. ### Response Format - All responses are JSON serializable. - Embedding vectors stored in Neo4j are never returned to the client. - Semantic search results are sorted by similarity score (descending). ## Recommended Usage: Use with uvx No installation needed! Use uvx to run the server in isolated environments. ## Configuration ### Environment Variables The following environmental variables must be configured for this LitCoin MCP Server: - OPENAI_API_KEY - OPENAI API Key for semantic search related tools such as get_semantic_similar_nodes and get_semantic_similar_edges. the embedding vectors for nodes and edges in the LitCoin KG were created using the `text-embedding-3-small` model from OpenAI. It's recommended to use the same model to generate embeddings for input text queries to ensure dimensional compatibility for semantic search related tools. - NEO4J_USERNAME - LitCoin Neo4j server authentication username. - NEO4J_PASSWORD - LitCoin Neo4…
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