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onto_search

Perform semantic search over an ontology using natural language queries to find classes by text meaning, structural position, or both.

Instructions

Semantic search over the loaded ontology using natural language. Returns the most similar classes by text meaning, structural position, or both. Requires onto_embed to have been run first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
alphaNoWeight for text vs structure in product mode (0.0-1.0). Default: 0.5
modeNoSearch mode: "text", "structure", or "product". Default: "product"
queryYesNatural language query
top_kNoNumber of results. Default: 10
Behavior3/5

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

No annotations are provided, so description carries full burden. It discloses that it performs semantic search and returns similar classes, but does not explicitly state that it is read-only or clarify other behavioral traits like object modification or rate limits.

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?

Two concise sentences, front-loaded with core purpose. No unnecessary words.

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?

Description adequately covers tool behavior and return value ('most similar classes'), plus prerequisite. With no output schema, it provides sufficient context for a search tool.

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?

Input schema has 100% coverage with descriptions for all 4 parameters. Description adds context about the embedding dependency but does not enhance parameter meaning beyond 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?

Description clearly states 'semantic search over the loaded ontology using natural language', specifying verb and resource. It distinguishes from sibling tools like onto_query and onto_reason by focusing on semantic similarity.

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

Usage Guidelines4/5

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

Explicitly states prerequisite 'Requires onto_embed to have been run first', providing clear usage context. However, it does not mention when not to use this tool or suggest 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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