ypollak2/llm-router

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully explains side effects (storage in 'routing_decisions' table), long-term machine learning impact (retraining the classifier), and return value semantics (confirmation or error). It does not mention idempotency or duplicate-rating behavior, leaving minor gaps.

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 docstring structure is exemplary: front-loaded summary sentence followed by mechanism, Args, and Returns sections. No sentences are wasted; the explanation of the retraining signal adds necessary context beyond the tautological 'rate decision' to justify why an agent should invoke this.

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 2-parameter feedback tool, the description is complete. It explains the data destination, the ML feedback loop, and acknowledges the existence of an output schema by describing the return type. The relationship to the broader routing ecosystem (implied by 'routing decisions') is sufficiently established.

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?

Given 0% schema description coverage, the Args section provides essential compensatory documentation: 'good' is explained as thumbs-up/down semantics, and 'decision_id' clarifies the None/default behavior for targeting the most recent decision. This fully addresses the schema's descriptive absence.

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 opens with a precise action ('Rate') and target ('routing decision'), clearly distinguishing this feedback tool from siblings like 'llm_route' (which likely creates decisions) and other llm_* utilities. The scope (good/bad rating) is immediately apparent.

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 establishes clear context by specifying this rates 'the last (or a specific) routing decision,' implying temporal usage after routing occurs. It explains the 'why' (retraining the classifier) but stops short of explicitly naming the sibling tool (e.g., llm_route) to call first or contrasting with alternatives.

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