semantic_search

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

With no annotations provided, the description carries the full burden. It explains the semantic matching behavior, notes that results are 'ranked by similarity score,' and gives illustrative examples. It does not cover edge cases or system limits, but the core behavioral traits are 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?

The description is three sentences long, front-loaded with the core purpose, followed by illustrative examples and usage guidance. Every sentence adds value with no redundancy or fluff.

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 tool has 6 parameters and no output schema. The description gives sufficient context for a semantic search tool, including what it returns ('concepts ranked by similarity score') and when to use it. It lacks detailed output structure, but that is not critical for understanding invocation.

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?

The input schema has 100% description coverage for all 6 parameters, so the description does not need to compensate. The description reinforces the query semantics with examples but does not add significant new detail beyond the schema's parameter descriptions.

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's purpose: 'Search for medical concepts using natural language with neural embeddings.' It differentiates from keyword search with concrete examples ('heart attack' finds 'Myocardial infarction'), making it distinct from 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 says 'Use this when the user describes symptoms, conditions, or treatments in everyday language rather than exact medical terminology.' This gives clear when-to-use guidance and implies when-not-to-use, though it does not name specific alternative tools.

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