Get Generated Content Summary

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

Annotations declare readOnlyHint=true, and the description aligns with that. The description adds the 'Anonymized' detail and clarifies the aggregated nature of the data, which is useful context beyond the annotation. No contradiction.

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 concise sentences with no filler. The first sentence front-loads the key outputs, the second gives a use case. Efficient and well-structured.

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 read-only summary tool with optional filters, the description covers the purpose, key outputs, and use case. It does not specify the output format, but since no output schema is provided, the description could benefit from that, though the core information needed for selection is present.

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?

Both parameters (limit and contentType) are described in the schema with 100% coverage. The description adds no additional parameter-specific semantics beyond what the schema already provides, so baseline 3 applies.

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 a specific verb 'Returns' and a specific resource 'aggregated stats on AI-generated content', listing concrete metrics (types, error targets, listening rates, TTS costs). It does not explicitly name a sibling, but the purpose is distinct enough from siblings like cl_get_tts_usage which focuses on TTS usage details.

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 a use case ('Useful for auditing the AI tutor's output quality and cost') but does not explicitly state when not to use it or mention alternative tools. The usage context is implied rather than explicitly contrasted with siblings.

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