find_similar_concepts

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

No annotations are provided, so the description bears the full burden of behavioral disclosure. It explains the three algorithms with trade-offs ('semantic' best for meaning, 'lexical' best for typos, 'hybrid' default), enforces the exactly-one-input constraint, and offers a practical tip about drug vocabulary usage. It does not describe output format or edge cases, but for a read-only similarity search this is a solid level of transparency.

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 compact and front-loaded: it opens with the core function, uses semicolons and em-dashes to pack algorithm details, and remains under three sentences. The final tip is extra but earns its place by providing practical guidance. 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 8 parameters and no output schema, the description covers the main decision points: algorithm selection, the three input modes, and intended use cases. Filters like domain_ids and vocabulary_ids are left to the schema, which adequately documents them. It could mention default thresholds or result ranking, but overall it is sufficiently complete for a search tool.

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%, so baseline is 3. The description adds meaningful value beyond the schema by explaining algorithm behavior, clarifying that query is natural language, and emphasizing the exactly-one-of constraint. The drug vocabulary tip also enriches parameter semantics, helping agents choose appropriate inputs for RxNorm concepts.

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 function: 'Find medical concepts similar to a reference concept, name, or natural language query.' It specifies the verb (Find), resource (medical concepts), and input types (concept_id, concept_name, query). It also distinguishes from siblings by emphasizing similarity search and naming three algorithms, plus concrete use cases like exploring related concepts and building phenotype sets.

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 provides clear usage context: 'Use this to explore related concepts, find alternative codes, or build phenotype concept sets.' However, it does not explicitly mention when not to use this tool or name alternatives like get_concept or search_concepts, even though sibling tools exist. The guidance is adequate but lacks direct exclusion or comparison.

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