recommend_model

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

With no annotations, the description carries the full burden. It discloses key behavioral traits: AI-powered ranking, prioritization of featured models, and task-based relevance scoring. However, it lacks details on rate limits, authentication needs, or response format, which are important for a recommendation tool.

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 front-loaded with the core purpose, followed by usage instructions and a key behavioral note (prioritization). Every sentence adds value without redundancy, making it 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 tool with 3 parameters, 100% schema coverage, and no output schema, the description is adequate but could be more complete. It explains the purpose and usage well but lacks details on response format, error handling, or how recommendations are generated, which would help an agent use it effectively.

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 schema fully documents parameters. The description adds minimal value beyond the schema, mentioning task description examples but not explaining parameter interactions or the effect of category/limit on recommendations.

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 specific action ('Get AI-powered model recommendations') and resource ('for a specific task'), with examples that distinguish it from siblings like list_models (which likely lists all models without recommendations) or generate_image (which executes generation rather than recommending models).

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 clear context for when to use this tool ('Describe what you want to do... and get the best-suited models'), but does not explicitly state when not to use it or name alternatives like list_models for unfiltered listings or generate_image for direct execution.

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