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

Annotations already convey readOnlyHint and openWorldHint, so the bar is lower. The description adds meaningful behavioral context beyond annotations: it discloses that over-loadLimit results are materialized as a dataframe and that the caller should use SQL via brapi_dataframe_query rather than paging row-by-row. This is useful, non-obvious behavior.

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 sentences, each earning its place: purpose, enrichment value, and overflow behavior with an explicit alternative query path. The most important information is front-loaded and there is no filler or repetition of schema content.

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 an 11-parameter tool with an output schema, no required parameters, and full schema descriptions, the description is complete enough. It covers the core discovery purpose, the enrichment behavior, and the important large-result-set fallback. The schema handles parameter-level detail, and the annotations cover safety and open-world semantics, so nothing critical 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 the baseline is 3. The description names a few high-level filter dimensions (crop, trial type, season, location, program) and mentions loadLimit behavior, but most parameter semantics are already fully documented in the schema. It adds minimal new meaning beyond what the property descriptions provide.

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 opens with a specific verb and resource: 'Locate studies matching crop, trial type, season, location, or program.' It further distinguishes the tool by noting it enriches results with program/trial/location context in one call, which clearly separates it from more generic raw-search or single-study getter siblings.

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 gives actionable usage context: use this tool to find studies with enriched context, and when the upstream total exceeds loadLimit, query the materialized dataframe with brapi_dataframe_query (SQL). It does not explicitly enumerate when to prefer brapi_get_study or brapi_raw_search, but the stated condition and enrichment benefit provide clear practical guidance.

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