memento-mcp

Instructions

Implementation Reference

• src/storage/neo4j/Neo4jStorageProvider.ts:1745-1786 (handler)

  The core implementation of getEntityEmbedding that queries Neo4j directly for the embedding vector stored on the Entity node.
  async getEntityEmbedding(entityName: string): Promise<EntityEmbedding | null> { try { // Verify that the entity exists const entity = await this.getEntity(entityName); if (!entity) { logger.debug(`Entity not found when retrieving embedding: ${entityName}`); return null; } const session = await this.connectionManager.getSession(); try { // Query to get the entity with its embedding const query = ` MATCH (e:Entity {name: $name}) WHERE e.validTo IS NULL RETURN e.embedding AS embedding `; const result = await session.run(query, { name: entityName }); if (result.records.length === 0 || !result.records[0].get('embedding')) { logger.debug(`No embedding found for entity: ${entityName}`); return null; } const embeddingVector = result.records[0].get('embedding'); // Return the embedding in the expected format return { vector: embeddingVector, model: 'unknown', // We don't store the model info in Neo4j lastUpdated: entity.updatedAt || Date.now(), }; } finally { await session.close(); } } catch (error) { logger.error(`Error retrieving embedding for entity ${entityName} from Neo4j`, error); return null; } }
• src/server/handlers/callToolHandler.ts:384-452 (handler)

  The MCP tool dispatch handler in callToolHandler that calls storageProvider.getEntityEmbedding and formats the response.
  case 'get_entity_embedding': try { // Check if entity exists const entity = await knowledgeGraphManager.openNodes([String(args.entity_name)]); if (!entity.entities || entity.entities.length === 0) { return { content: [{ type: 'text', text: `Entity not found: ${args.entity_name}` }] }; } // Access the embedding using appropriate interface if ( knowledgeGraphManager.storageProvider && typeof (knowledgeGraphManager.storageProvider as Record<string, unknown>) .getEntityEmbedding === 'function' ) { type EntityEmbedding = { vector: number[]; model?: string; lastUpdated?: number; }; const embedding = await ( knowledgeGraphManager.storageProvider as Record< string, (entityName: string) => Promise<EntityEmbedding | null> > ).getEntityEmbedding(String(args.entity_name)); if (!embedding) { return { content: [ { type: 'text', text: `No embedding found for entity: ${args.entity_name}` }, ], }; } return { content: [ { type: 'text', text: JSON.stringify( { entityName: args.entity_name, embedding: embedding.vector, model: embedding.model || 'unknown', dimensions: embedding.vector ? embedding.vector.length : 0, lastUpdated: embedding.lastUpdated || Date.now(), }, null, 2 ), }, ], }; } else { return { content: [ { type: 'text', text: `Embedding retrieval not supported by this storage provider`, }, ], }; } } catch (error: Error | unknown) { const errorMessage = error instanceof Error ? error.message : String(error); return { content: [{ type: 'text', text: `Error retrieving entity embedding: ${errorMessage}` }], }; }
• src/server/handlers/listToolsHandler.ts:389-403 (registration)

  Tool registration including name, description, and input schema in listToolsHandler.
  { name: 'get_entity_embedding', description: 'Get the vector embedding for a specific entity from your Memento MCP knowledge graph memory', inputSchema: { type: 'object', properties: { entity_name: { type: 'string', description: 'The name of the entity to get the embedding for', }, }, required: ['entity_name'], }, },
• src/server/handlers/listToolsHandler.ts:393-403 (schema)

  The input schema definition for the get_entity_embedding tool.
  inputSchema: { type: 'object', properties: { entity_name: { type: 'string', description: 'The name of the entity to get the embedding for', }, }, required: ['entity_name'], }, },