recommend_model

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 states the tool returns recommendations 'ranked by relevance,' which is informative for a non-destructive, read-only tool. However, it does not describe any potential side effects, rate limits, or specific output format. Given the tool's simplicity and safety, a score of 3 is appropriate—it adds useful context but lacks deeper behavioral detail.

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 consists of two concise sentences. The first states the function, and the second provides usage examples. Every word adds value; there is no redundancy or unnecessary detail. It is appropriately sized for a simple tool and is front-loaded with the core purpose.

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 low complexity (one parameter, no nested objects) and full schema coverage, the description adequately explains the input and purpose. However, it lacks details about the output (e.g., what fields are returned, how many recommendations) and does not mention any prerequisites or conditions. Since there is no output schema, the description should be more complete about the return value. A score of 3 reflects that it is adequate but has clear gaps.

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?

The input schema has 100% coverage with a single parameter 'task' described as 'What you want to do...' The tool description enhances this by providing natural language examples and specifying that recommendations are 'ranked by relevance,' which adds semantic value beyond the schema. Although the schema already covers the parameter well, the description's examples and purpose statement earn a score of 4.

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 function: 'get model recommendations ranked by relevance' based on a natural language description of a task. The examples ('remove background from product photos', 'generate a 10-second cinematic video') clarify the input type. This distinguishes it from siblings like 'search_models' (keyword search) and 'explore' (browsing), as it is specifically for task-driven recommendations.

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 explicitly instructs users to describe what they want to build using natural language, with concrete examples. It implies the tool is for users who need model recommendations based on a task description. While it does not explicitly exclude alternatives or mention when not to use it, the context is clear enough. A score of 4 reflects clear usage context without explicit exclusions.

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