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neo4j-field

Neo4j GraphRAG MCP Server

by neo4j-field

Search-Augmented Cypher Query

search_cypher_query
Read-onlyIdempotent

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.
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover key behavioral traits (read-only, non-destructive, idempotent, open-world). The description adds valuable context beyond annotations: it explains the tool's power in handling large result sets (100-1000 items), combining search with graph traversal, and using placeholders for embeddings/text. It doesn't mention rate limits or auth needs, but with annotations providing safety profile, this is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear purpose statement, bulleted capabilities, an example, and placeholder explanations. It's appropriately sized for a complex tool, though the bulleted list and example make it slightly longer than minimal. Every section adds value, but it could be more front-loaded with key usage guidance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (4 parameters, no output schema) and rich annotations, the description is mostly complete. It covers purpose, usage, capabilities, and placeholders. However, it lacks details on error handling, result format, or performance considerations, which would be helpful for a tool with open-world and idempotent hints.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema: it mentions placeholders ($vector_embedding, $fulltext_text) and provides an example, but doesn't explain parameter interactions or constraints not in the schema. Baseline 3 is appropriate when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool's purpose: 'Execute a Cypher query that uses vector and/or fulltext search indexes.' It specifies the action (execute), resource (Cypher query), and key capabilities (vector/fulltext search), clearly distinguishing it from sibling tools like read_neo4j_cypher (general queries) and vector_search/fulltext_search (standalone searches).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool: for queries combining search with graph traversal, post-filtering large results, and aggregating over search results. It implicitly suggests alternatives by naming sibling tools (fulltext_search, vector_search) for simpler search-only tasks, though it doesn't explicitly say 'use X instead of Y.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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