modelradar_recommend

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

With no annotations, the description carries the transparency burden. It discloses the core behavior: ranks models, returns live OpenRouter pricing and a rationale, and produces an openrouter_id for routing. It does not disclose ranking methodology, potential side effects, or dependencies (e.g., network calls, authentication). This is a moderate level of disclosure, earning a 3.

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 a single, well-structured sentence that front-loads the purpose and immediately follows with the key output details and a call-to-action. Every clause adds value—what the tool does, what it returns, and how to use the result. There is no fluff or repetition.

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 8 parameters, no output schema, and no annotations, making it relatively complex. The description provides a high-level purpose and output shape but does not explain how the recommendation works, how parameters influence it, what the returned object looks like beyond openrouter_id, or any usage caveats. It is not complete enough to safely invoke the tool with complex intents.

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 coverage is only 38%, so the description must compensate. It does not explain parameters like limit, prefer, multimodal, min_context, open_weights, or max_input_price beyond what the schema already provides. The only indirect meaning is that 'need' relates to the task/query, and the output references openrouter_id, which clarifies why one would use the result. This is insufficient compensation for the low schema coverage, so a score of 2 is appropriate.

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 job: recommend the best OpenRouter-routable model(s) for a need, with a specific verb ('Recommend') and resource ('OpenRouter-routable model(s)'). It adds context about returning ranked models with pricing and rationale, but does not explicitly contrast with sibling tools like modelradar_search or modelradar_get, earning a 4 rather than a 5.

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: when you need a model recommendation, it returns ranked options and tells you to route your CLI to the openrouter_id. It provides a clear next step but does not explicitly state when to use this tool versus alternatives, nor mention exclusions or prerequisites. This is implied usage guidance, not explicit.

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