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grafeo-mcp

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by GrafeoDB

search_text

Search nodes by keyword or phrase in a specified text property. Requires a pre-existing text index; returns matching results sorted by relevance.

Instructions

Full-text keyword search over string properties.

Finds nodes whose text property matches the search query. Requires a text index created via create_text_index on the same label + property.

Use this tool when: you want to search for nodes by keyword or phrase. Do NOT use for: semantic/vector similarity (use vector_search), exact property matching (use execute_gql with WHERE), or browsing by label (use search_nodes_by_label).

Args: label: Node label to search within (e.g. "Article"). property: String property to search (e.g. "title"). query: Search query string (keywords or phrase). limit: Maximum number of results to return (default 20).

Returns: JSON array of {node_id, score, labels, properties} sorted by relevance score descending.

Examples: search_text("Article", "title", "graph database") search_text("Document", "content", "machine learning", limit=10)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelYes
propertyYes
queryYes
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Discloses dependency on create_text_index, describes return format (JSON array sorted by relevance), but does not explicitly state that the tool is non-destructive (read-only). Annotations absent, so description carries full burden.

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?

Well-structured with purpose, prerequisites, usage guidance, parameter details, return description, and examples. Every sentence adds value; concise and front-loaded.

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 (4 params, output schema exists), the description covers prerequisites, usage, return format, and examples completely. No gaps identified.

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?

With 0% schema coverage, description compensates by detailing each parameter (label, property, query, limit) with context (e.g., 'Node label to search within') and examples, adding significant meaning beyond the 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 starts with 'Full-text keyword search over string properties' clearly stating the action and resource. It distinguishes from siblings like vector_search and search_nodes_by_label, providing specific verb+resource.

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?

Explicitly states when to use ('you want to search for nodes by keyword or phrase') and when not to use (vector_search, execute_gql, search_nodes_by_label), giving clear alternatives.

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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