semantic_search

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

No annotations are provided, so the description carries the full burden. It explains that it uses neural embeddings, returns concepts ranked by similarity score, and understands clinical meaning. It does not detail auth needs or rate limits, but for a search tool, the core behavior is well disclosed.

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

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

Three sentences that front-load the action, provide concrete examples, and include usage guidance. Every sentence adds value with no wasted words.

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

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

Given no output schema, the description explains returns ('concepts ranked by similarity score') and core behavior. It covers the key aspects needed for an agent to understand when and how to invoke the tool, though it could mention pagination or error handling.

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

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

Schema coverage is 100%, so all parameters are described in the schema. The description adds overall context (e.g., threshold controls strictness) but does not add significant new meaning beyond what the schema provides for each parameter. Baseline 3 is appropriate.

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

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

The description clearly states the tool searches medical concepts using neural embeddings, with concrete examples like 'heart attack' finding 'Myocardial infarction', distinguishing it from keyword search. It is specific and contrasts with sibling tools like search_concepts.

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

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

The description explicitly advises when to use this tool: 'Use this when the user describes symptoms, conditions, or treatments in everyday language rather than exact medical terminology.' It implies alternatives via 'Unlike keyword search,' but does not name specific sibling tools.

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