select_model_for_project

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 output format ('concise markdown plus structured fields') and the decision dimensions (candidates, scores, reasons, pricing, tradeoffs). This adds useful behavioral context beyond the schema, though it doesn't discuss any potential side effects or limitations.

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?

Two sentences with no wasted words. The first sentence states the purpose, the second gives usage context and output summary. It is well front-loaded and earns every word.

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 7 optional parameters, nested objects, and an output schema, the description covers the core decision role, usage context, and return format. Combined with the output schema, the agent has sufficient context to invoke this tool correctly without major 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?

Schema description coverage is 100%, so the baseline is 3. The tool description does not add additional parameter-level meaning beyond the schema, which is acceptable given the schema fully documents all seven parameters with examples and enums.

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 begins with a specific verb+resource: 'Pick the best overall, cheapest acceptable, and best value AI model for a project.' It clearly distinguishes itself from sibling tools by framing it as the consolidated decision tool versus manually chaining recommend, compare, pricing, and cost tools.

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?

Explicitly states when to use: 'Use this when an agent has project context and needs one decision' and contrasts it with manually chaining other tools. While it doesn't name alternative sibling tools by name, the 'instead of manually chaining' provides clear usage context and implies the excluded approach.

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