llm_rate

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

No annotations are provided, so the description carries full burden. It discloses storage in the 'routing_decisions' table, use for retraining the classifier, and return of confirmation or error. This is comprehensive for the tool's simple behavior.

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 well-structured with a brief summary, an args block, and a returns block. Every sentence adds value, and it is appropriately concise for the tool's simplicity.

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 has only two parameters and no nested objects, the description covers all necessary information: parameter semantics, behavioral effects, and return value. Nothing is missing.

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%, but the description explains both parameters: good (boolean) and decision_id (integer or null, default null). It adds meaning beyond the schema's type constraints.

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 rates a routing decision as good or bad, storing thumbs-up/down feedback. It distinguishes from sibling tools like llm_reroute or llm_approve_route by focusing on post-hoc feedback collection.

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 explains how to use it: rate the last decision by omitting decision_id or a specific one by providing it. It doesn't explicitly mention when not to use it, but the context is clear for feedback after a routing decision.

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