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

Annotations already declare readOnlyHint=true, and the description's 'Non-mutating computation' largely restates that. What it does add is the NLP nature and that mentions carry 'states', but it omits any disclosure of return shape, latency, or auth needs. With annotations covering the safety profile, this is adequate but not rich.

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?

Three tight sentences with the core action front-loaded. 'Engine API: POST /parse' is mildly meta/redundant but does confirm the endpoint, and nothing pads the text.

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 non-mutating parse tool with 100% schema coverage and no output schema, the description covers purpose and output nature (mentions, states, evidence ids). It would be stronger with a note on the relationship between `context` and results, but nothing essential to correct invocation is missing.

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 all nine parameters are documented in the schema itself. The description adds nothing parameter-specific (e.g., how context ids interact with parsing), so the baseline 3 applies.

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?

Specific verb+resource: 'Extract clinical mentions (symptoms / risk factors) and their states from free-text' and states the output form (mapped to Infermedica evidence ids via NLP). This is clearly distinguishable from listing/getter siblings, though it never explicitly contrasts with infermedica_search_concepts, which also maps text to ids.

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?

Usage is implied by 'free-text describing complaints' and 'Engine API: POST /parse', giving context of the parsing step, but there is no when-to-use/when-not guidance and no routing against alternatives like search_concepts or suggest. The agent must infer when parsing is appropriate.

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