Generate text

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

No annotations provided, so description carries full burden. Only mentions it returns generated text, but fails to disclose cost, latency, error handling, or model-specific differences. Lacks behavioral context.

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?

Very concise single sentence, but at the expense of missing important details. Balanced conciseness with clarity, but could be restructured to front-load core action.

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 no output schema and limited annotations, the description lacks details on return format, streaming, or handling large outputs. Does not mention that output might be a string or JSON. Sibling tools like prompting_guide suggest more context exists elsewhere.

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 50%; the model parameter has a description with an example, which is helpful. The prompt parameter lacks description, and the tool description does not compensate. No output schema, so return format is unclear.

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?

Description clearly states the tool runs a text/LLM model and lists specific model examples. It distinguishes from sibling tools like generate_image and generate_video by focusing on text generation.

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. Does not mention prerequisites, model selection criteria, or scenarios where other tools (e.g., prompting_guide) might be better suited.

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