generate_text

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context beyond the input schema, such as the payment requirement, file attachment support, and image analysis limitations (e.g., 'Best/K2.5 model only'). However, it doesn't cover other behavioral traits like rate limits, error handling, or response format, leaving gaps for a tool with 8 parameters and no output schema.

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 appropriately sized and front-loaded, starting with the core purpose and then listing key features and requirements. Each sentence adds value, such as specifying capabilities and constraints, with no redundant information. However, it could be slightly more structured by separating usage notes from requirements.

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 tool's complexity (8 parameters, no output schema, and no annotations), the description is moderately complete. It covers the purpose, key capabilities, and a critical requirement (payment), but lacks details on behavioral aspects like response format, error cases, or performance characteristics. This leaves gaps for an AI agent to fully understand tool behavior without trial and error.

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 100%, so the input schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning file attachments and image analysis in general terms, but it doesn't provide additional semantic details or clarify parameter interactions (e.g., how 'fileContext' and 'imageBase64' relate to 'modelId'). Baseline 3 is appropriate given high schema coverage.

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's purpose: 'Generate text using AI language models.' It specifies the core action (generate text) and resource (AI language models), which is more specific than just restating the name. However, it doesn't explicitly differentiate from sibling tools like 'generate_3d_model' or 'generate_image' beyond mentioning 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?

The description provides some implied usage context by mentioning requirements ('Requires a valid paid payment ID') and capabilities ('Supports file attachments... and image analysis...'), but it doesn't explicitly state when to use this tool versus alternatives like 'analyze_image' for vision tasks or 'list_models' for model selection. No clear exclusions or named alternatives are provided.

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