benchmark_data

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

With no annotations, the description should reveal behavior beyond input/output. It does not mention whether the tool is read-only, how it handles missing values, or any required data formats. Only output structure is provided.

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?

Two sentences, front-loaded with purpose and output fields. No superfluous information.

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?

Adequate for a straightforward tool with output schema. Missing details on error handling, data requirements, and best practices, but sufficient for basic understanding.

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% with basic descriptions. The tool description adds context by giving examples of benchmarks (EU average, regional, custom), augmenting the 'benchmarks' parameter's meaning. This adds value 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 states the tool compares data against benchmarks and lists return fields. It gives examples like EU average, regional, custom, which clarifies scope. However, it could be more precise about what 'compare' entails (e.g., statistical analysis).

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?

No guidance on when to use this tool versus alternatives like compare_datasets or compute_metrics. The description does not mention prerequisites or exclusions.

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