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GrafeoDB

grafeo-mcp

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by GrafeoDB

execute_gql

Execute custom GQL queries for graph traversal, aggregation, and mutations. Supports Cypher-compatible syntax for multi-hop patterns and data manipulation.

Instructions

Execute a GQL query against the graph database.

GQL (Graph Query Language) is the ISO/IEC standard query language. Use MATCH patterns to find nodes and relationships, INSERT to add data, and RETURN to project results.

Cypher syntax (e.g. CREATE) is automatically normalized to GQL (INSERT), so queries written in Cypher style will generally work as-is.

Use this tool when: you need to run a custom query — complex filters, multi-hop traversals, aggregations, or mutations beyond what the CRUD tools (create_node, create_edge) provide. Do NOT use this for: simple node lookups (use get_node), label browsing (use search_nodes_by_label), or one-hop exploration (use get_neighbors).

Args: query: A GQL query string (Cypher syntax is auto-normalized). limit: Maximum rows to return (default 100). Use to prevent overwhelming context windows. The query itself can also contain a LIMIT clause for server-side limiting.

Examples: MATCH (p:Person) RETURN p.name, p.age MATCH (a:Person)-[:KNOWS]->(b:Person) RETURN a.name, b.name INSERT (:Person {name: 'Alice', age: 30}) MATCH (p:Person) WHERE p.age > 25 RETURN p.name LIMIT 10

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It mentions Cypher auto-normalization and the limit parameter. However, it does not detail error behavior, potential side effects of mutations, or authentication requirements. This is a minor gap, but overall good.

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

Conciseness5/5

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

The description is well-structured with sections (overview, usage guidelines, args, examples). Every sentence adds value; there is no redundancy. The length is justified by the complexity of the tool.

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

Completeness5/5

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

Given the tool's complexity and the presence of an output schema (not requiring return value explanation), the description covers purpose, usage, parameters, and examples comprehensively. It is complete for an AI agent to understand when and how to invoke it.

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

Parameters5/5

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

Despite 0% schema description coverage, the description's 'Args' section provides comprehensive explanations for both parameters: query (with examples of GQL/Cypher syntax) and limit (purpose, default, and advice on also using LIMIT in the query). This adds significant meaning beyond the basic schema.

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 clearly states the tool's purpose: 'Execute a GQL query against the graph database.' It elaborates on GQL, mentions Cypher normalization, and provides concrete examples. It distinguishes itself from sibling tools by specifying when to use this tool vs. CRUD tools.

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 explicitly outlines when to use the tool ('complex filters, multi-hop traversals, aggregations, or mutations beyond what the CRUD tools provide') and when not to use it ('simple node lookups', 'label browsing', 'one-hop exploration'). It references alternative tools, providing clear guidance.

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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