Archive career aggregates

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, but the description adds valuable caveats: serve stats only exist from 1991, matches_with_stats quantifies coverage, ambiguous names yield candidate lists, and a plan requirement exists. These go beyond the structured 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 information-dense but well-organized, front-loading the core purpose and then adding necessary caveats. Every clause adds value, with no redundancy or filler.

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?

The description covers data scope, statistics included, coverage limitations, ambiguity resolution, and plan requirements. With an output schema present, detailing return values is unnecessary, making this complete for a tool of this 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?

The input schema already describes the 'name' parameter with minLength and 'must resolve to exactly one person.' The description adds the behavioral detail that an ambiguous fragment returns a candidate list, which is not in the schema, thus adding meaning beyond the schema.

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 one player's whole career aggregates from the results archive, listing specific statistics (W-L by surface/level/year, titles, serve stats) and the date range (1968–2022). This distinct focus differentiates it from siblings like get_player or get_archive_match.

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?

Provides clear context for when to use the tool (career-level aggregation) and a key constraint: the name must resolve to exactly one person, with ambiguous fragments returning a candidate list. It does not explicitly name alternative tools or state when not to use this over others, but the context is sufficient.

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