find_similar_concepts

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

With no annotations, the description carries full burden. It describes the algorithm behavior, input constraints, and a vocabulary-specific tip. It does not mention destructive actions (none expected) or output format, but is transparent about what the tool does.

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 a single well-organized paragraph: purpose, algorithms, input requirement, use cases, and a tip. No redundant sentences. Efficiently conveys all necessary information.

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 8 parameters, no output schema, and no annotations, the description covers core functionality, input constraints, algorithm choices, and a practical tip. It could mention pagination or result structure, but the schema's page_size description suffices. Overall, it provides enough context for effective use.

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?

All 8 parameters are described in the schema (100% coverage), so baseline is 3. The description adds value by explaining the constraint of providing exactly one input, the algorithm meanings, and a practical tip for drug vocabularies, going beyond schema 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 finds similar medical concepts, lists three algorithms, and gives specific use cases like exploring related concepts or building phenotype sets. It differentiates from siblings by focusing on similarity rather than exact search.

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 to provide exactly one of concept_id, concept_name, or query, and gives a tip for drug vocabularies. It lacks direct comparison to siblings but implies when to use via use-case examples.

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