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quipu_search_nodes

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Search knowledge graph entities using natural language queries by matching names, labels, and values, with optional group and type filters to narrow results.

Instructions

Search for entities in the knowledge graph by natural language query. Uses text matching on entity names, labels, and values. Replaces Graphiti's search_nodes.

Input Schema

NameRequiredDescriptionDefault
queryYesNatural language search query
verboseNoReturn expanded full IRIs instead of default CURIE-compacted values.
group_idsNoOptional: filter to entities from these knowledge graph groups
max_resultsNoMaximum results (default: 10)
entity_type_filterNoOptional: filter by rdf:type IRI

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.17.0

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description usefully adds that matching is lexical over entity names/labels/values and that the scope is entities rather than facts or episodes, but says nothing about ranking, result shape, or truncation behavior beyond the schema's max_results.

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?

Three short sentences with the core purpose front-loaded and no filler. Slightly more could be trimmed from the Graphiti migration note, which serves users rather than agents.

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

Completeness3/5

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

Five parameters are all documented in the schema and there is no output schema, so the description needn't explain returns. What remains missing is placement among the crowded search family and any hint about result ordering or limits in practice.

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 explains query, verbose, group_ids, max_results, and entity_type_filter. The description adds no extra semantics — notably it never mentions the CURIE-vs-IRI toggle or the group/type scoping that would help an agent craft calls.

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

Purpose4/5

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

States a specific verb (search) and resource (entities in the knowledge graph) plus the retrieval mechanism (text matching on names, labels, values). The 'replaces Graphiti's search_nodes' line aids migration but doesn't differentiate it from the many sibling search tools by name.

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

Usage Guidelines3/5

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

The 'text matching' note implies this is the lexical route rather than a semantic/hybrid one, which gives the agent some basis for choosing. However, no sibling is named and there is no explicit when-to-use/when-not-to-use guidance against quipu_hybrid_search, quipu_unified_search, quipu_search_facts, or quipu_search_query.

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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