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. It discloses the search scope (1000+ models, 5 providers) and the ranking criteria (task match, capabilities, quality tier, price, speed, benchmarks). This goes beyond mere intent but does not explicitly state whether the operation is read-only or if any side effects exist. The implied read-only nature is clear enough 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 two sentences, front-loads the core purpose, and packs relevant details (providers, categories, ranking criteria) without repetition or fluff. Every sentence contributes useful information.

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?

The tool has 4 parameters, no annotations, and no output schema. The description explains the return value (ranked results) and the basis for ranking, covering the main need. It does not mention pagination, error handling, or exact output structure, but for a recommendation tool with a broad search, the description is sufficiently complete.

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 baseline is 3. The description adds general context about the search scope and ranking, which indirectly relates to the task and budget parameters, but it does not provide specific semantic value beyond what the schema already describes for each parameter. It fails to compensate or extend the parameter-level guidance.

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: recommend the best AI model for a task. It uses specific verbs ('Recommend', 'Searches'), defines the resource (AI models across 1000+ models from 5 providers), and distinguishes itself from siblings like compare_models or list_models by emphasizing task-based ranked search.

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 implies usage context: use when you need a task-based recommendation without preselecting models. It gives clear context (searches a broad catalog, ranks results) but does not explicitly mention when not to use it or reference alternatives like compare_models or select_model_for_project. There are no exclusions or alternative guidance, so it stops short of a 5.

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