benchmark_series

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

The description discloses the cost (1 credit) but does not mention whether the operation is read-only or has side effects. With no annotations provided, the description carries the full burden, and it falls short of fully describing behavioral traits such as safety or latency.

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: the first states purpose and cost, the second lists benchmark keys. It is front-loaded and contains no unnecessary words, making it efficient for an agent to parse.

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 absence of an output schema and annotations, the description does not explain the return format (e.g., time series points) or provide details on pagination, rate limits, or completeness. It is adequate but leaves gaps for an agent to infer.

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 100% description coverage, but the description adds value by enumerating all valid benchmark keys (swe_bench, mmlu_pro, gpqa_diamond, math, human_eval), which the schema only provides as examples. This clarifies the exact input set 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 provides 'score evolution for a single benchmark on one AI model', which is a specific verb and resource. It also mentions cost and lists valid benchmark keys, distinguishing it from sibling tools like compare_models or cost_projection.

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 indicates the cost (1 credit) and lists valid benchmark keys, providing some usage context. However, it does not explicitly state when to use this tool versus alternatives like compare_models, nor does it mention any exclusions or prerequisites.

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