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 the neural embedding approach and ranking by similarity score, but does not disclose whether it is read-only, any authentication needs, rate limits, or safety characteristics. It adds value by explaining semantic understanding, but lacks comprehensive behavioral context.

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?

The description is concise with two core sentences, followed by examples and usage guidance. Every sentence adds value, and the structure is clear and front-loaded. No unnecessary 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?

The description explains the return value as 'concepts ranked by similarity score,' which is adequate for a search tool. However, with no output schema, more detail on result fields (e.g., concept ID, name) would improve completeness. Given 6 parameters and the overall complexity, it is nearly complete.

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 description coverage is 100%, baseline 3. The description adds meaningful examples for vocabulary_ids and domain_ids, explains the threshold parameter with a default, and clarifies page_size. This enhances understanding beyond the schema alone, pushing the score to 4.

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 for medical concepts using natural language with neural embeddings. It provides examples ('heart attack' finds 'Myocardial infarction'), distinguishing it from keyword search. The verb 'search' and resource 'medical concepts' are specific.

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 says when to use this tool: 'when the user describes symptoms, conditions, or treatments in everyday language.' It contrasts with keyword search implicitly but does not explicitly name alternative tools like search_concepts or mention when not to use it, leaving room for slight improvement.

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