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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds valuable context: no authentication required, the data source (BDL API v1), and the output format (numeric variable_id and measurement unit). It also clarifies its role as a lookup step, which goes beyond the annotations.

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 two sentences with no filler. The first sentence encapsulates the two operational modes and the second covers output, downstream use, data source, and auth. Information is front-loaded and every clause contributes meaning.

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?

With an output schema, return values do not need elaboration, but the description still mentions key output fields. It references sibling tools for subjects and data, though the data reference is slightly off. Pagination and mutual exclusivity of parameters are covered in the schema, so the description is nearly complete for its complexity.

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%, so the baseline is 3. The description enhances parameter understanding by framing the two modes ('search by keyword' vs 'list all under subject_id') and by providing concrete keyword examples. This adds semantic clarity beyond the schema's per-parameter 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 states a clear, specific purpose: search statistical variables by keyword or list all under a subject. It distinguishes the tool from siblings by noting its output (variable IDs and units) and its role as a precursor to fetching data. Examples of keywords and the explicit mention of the subject source make the purpose unmistakable.

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 explains two usage modes and references the subjects tool for obtaining subject IDs. However, it directs users to 'gus-poland.data' for downstream data, but the sibling list contains 'gus-poland.series.data' instead. This mismatch could mislead an agent into looking for a non-existent tool, weakening the routing guidance.

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