recommend_model

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the return includes ranked models with pricing, strengths, and caveats, implying a read-only operation. However, it does not explicitly mention safety, rate limits, or any potential side effects, leaving some ambiguity.

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 extremely concise with three sentences, each delivering distinct value: purpose, usage tip, and output summary. No extraneous words, and the most critical information appears first.

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 complexity (7 parameters, no annotations) and the presence of an output schema, the description lacks parameter explanations. It does provide a usage link to 'analyze_task', partially compensating. Still, the agent would need to infer parameter semantics from names, which is a significant gap.

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 0%, and the description does not explain the 7 parameters (use_case, domain, scale, etc.) beyond passing them from 'analyze_task'. The agent must infer meanings from parameter names alone, which is insufficient for correct invocation.

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 recommends ranked open-source and closed-source models for a task, specifying the verb 'recommend', resource 'models', and scope 'open-source and closed-source'. This distinguishes it from sibling tools like 'analyze_task' (analyzes task) and 'compare_cloud_vs_onprem' (compares hosting).

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 advises passing parameters from 'analyze_task' output for best results, providing a context for use. However, it does not explicitly state when not to use this tool or mention alternatives like 'compare_cloud_vs_onprem' or 'estimate_inference_cost' that might be more appropriate for specific use cases.

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