Skip to main content
Glama

search_cypher_query

Execute Cypher queries with vector and fulltext search capabilities to retrieve and analyze graph data for GraphRAG applications.

Instructions

Execute a Cypher query that uses vector and/or fulltext search indexes.

This powerful tool allows you to:

  1. Use vector search ($vector_embedding) and/or fulltext search ($fulltext_text) in Cypher

  2. Post-filter large result sets (fetch 100-1000, filter with WHERE)

  3. Combine search with graph traversal

  4. Aggregate over search results

Example:

search_cypher_query( cypher_query=''' CALL db.index.vector.queryNodes('chunk_embedding_vector', 500, $vector_embedding) YIELD node, score WHERE score > 0.75 MATCH (node)-[:BELONGS_TO]->(d:Document) WHERE d.year >= 2020 RETURN node.chunkId, d.title, score ORDER BY score DESC LIMIT 20 ''', vector_query="student requirements" )

Placeholders:

  • $vector_embedding: Replaced with embedding vector

  • $fulltext_text: Replaced with text string for fulltext

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cypher_queryYesCypher query using $vector_embedding and/or $fulltext_text placeholders.
vector_queryNoText query to embed for vector search. Use $vector_embedding placeholder in Cypher.
fulltext_queryNoText query for fulltext search. Use $fulltext_text placeholder in Cypher.
paramsNoAdditional parameters for the Cypher query.

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/guerinjeanmarc/mcp-neo4j-graphrag'

If you have feedback or need assistance with the MCP directory API, please join our Discord server