route_model

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

With no annotations provided, the description carries the full burden. It discloses that the tool internally scores using 'Logic', maps to a model, and provides cost savings, and costs 1 token. This is fairly transparent, though it could mention the exact output format (e.g., a string recommendation or structured data).

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 three sentences long, contains no redundant information, and front-loads the core purpose. Every sentence adds distinct value: purpose, internal mechanism, and cost. It is optimally concise.

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 tool's simplicity (1 param, no output schema, no annotations), the description covers purpose, internal logic, and cost. However, it could be enhanced by specifying what the output looks like (e.g., a model name string) and how cost savings are presented. Overall, it is sufficiently complete for a basic recommendation tool.

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 schema coverage is 100% with one parameter 'text' described as 'The prompt text to evaluate'. The description adds value by explaining the purpose (routing based on reasoning complexity) beyond the schema's minimal description. It provides semantic context that aids in parameter usage.

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 which Claude model to use based on reasoning complexity, and distinguishes itself from siblings like score_prompt (which likely only scores) and check_usage (which likely checks usage limits). The verb 'recommend' and resource 'model' are specific, and the description explains the internal process.

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) but does not explicitly state when to use this tool versus alternatives like score_prompt or check_usage. No exclusions or when-not-to-use guidance is provided, which is a gap given the sibling tools.

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