sbb_get_passenger_frequency

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

Annotations already indicate read-only and idempotent behavior. The description adds that the dataset is updated yearly and describes the exact data fields returned, adding value beyond 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 well-structured: a clear opening sentence, then details about the dataset, a structured parameter list, and return schema. Every sentence is relevant and no fluff.

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 the complexity (7 sub-parameters, output schema), the description covers data types, pagination, response formats, and update frequency. It could mention potential errors or authentication but annotations provide safety cues.

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?

The input schema has no description for the top-level 'params' parameter (0% coverage). The description fills this gap by listing and explaining each sub-parameter (station_name, canton, year, etc.), providing meaningful usage guidance.

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 retrieves passenger frequency data for SBB stations, specifying the metrics (DTV, DWV, DNWV). It distinguishes from siblings like sbb_compare_stations through the specific data type, but does not explicitly differentiate.

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 context (yearly update, data types) but does not explicitly state when to use this tool over alternatives like sbb_compare_stations or sbb_list_datasets.

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