codebench_query

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

With no annotations, the description carries the behavioral burden and covers the important traits: table orientation for each group_by value, 'Returns one table per run/row_label,' inclusive date filters, and the notable last_n behavior ('0 means NO runs; omit it for no limit; negative is an error'). It does not explicitly state that the operation is read-only, but 'query' strongly implies it and output behavior is well specified.

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 efficiently organized: a one-line purpose, two compact group_by mode explanations, then a bulleted parameter list. Every sentence adds information, including the necessary warning about last_n's historical negative-value semantics, with no 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?

For a nine-parameter query tool, the description covers all inputs, defaults, pivot behavior, and output format. Since an output schema exists, the return shape does not need to be restated. Nothing needed for an agent to call this tool correctly 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 0%, so the description must compensate, and it does: it documents all nine parameters with defaults ('runs default: all matching', 'format default json'), value domains ('row' or 'run', 'json' or 'csv'), date format, inclusive date semantics, and the last_n corner-case. This goes well beyond what the bare JSON schema provides.

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 'Query and pivot benchmark results,' a specific verb+resource statement, and then distinguishes the tool from benchmark-management siblings by explaining the two pivot modes and their return shapes. An agent can tell this apart from codebench_list, codebench_delete, and codebench_import without opening the schema.

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 clear context for when to use it (querying/pivoting benchmark results) and explains how the two group_by modes change the returned table shape. It does not explicitly name alternatives or state when not to use it, but the purpose is unambiguous and the grouping semantics provide practical usage guidance.

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