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 usefully discloses what the response contains (hierarchy chain, children, aggregate drug and trial counts), which is real behavioral context, but says nothing about permissions, errors, or whether missing ids are handled.

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?

One dense line, front-loaded with the look-up key followed by the returned content. No filler sentences; every clause carries 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?

For a low-risk read tool with one documented parameter and no output schema, the description adequately enumerates what comes back, compensating for the absent output schema. Minor gaps are auth/error behavior, which are unlikely to block correct 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?

Schema coverage is 100% and the single parameter is documented as taking an id produced by search_indications. The description adds no syntax, uniqueness, or format detail beyond that, so it matches the baseline for full-coverage schemas.

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?

States a specific verb+resource (取适应症 by indication_id) and enumerates the response payload (parent chain, children, drug count, trial count). It follows the get_*_detail sibling pattern but never explicitly contrasts itself with search_indications, so differentiation is only implied by naming convention.

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?

No explicit when-to-use statement, but the parameter description ('search_indications 返回的 id') implies the intended flow: search first, then fetch detail by id. Boundaries with siblings remain for the agent to infer.

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