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. It successfully discloses model routing behavior ('routes to the best generation model') and cost implications (simple vs. premium models based on complexity). However, it omits safety profiles, rate limits, idempotency, or error handling behavior that would be necessary for a higher score.

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 clear purpose statement upfront, followed by use-case guidance, then parameter details. While the Args section is slightly verbose, every sentence provides necessary information given the lack of schema documentation, making it appropriately sized for its complexity.

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 6 parameters with zero schema coverage, the description successfully documents all inputs and their interactions (e.g., complexity affecting model selection). The presence of an output schema reduces the burden to describe return values. Minor gaps remain around rate limits and explicit safety declarations, but it is otherwise complete.

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 has 0% description coverage, but the description fully compensates by providing rich semantics for all 6 parameters. Notably, it explains the business logic for 'complexity' (drives model selection and cost), clarifies that 'temperature' affects creativity, and defines 'context' as conversation history—adding meaning beyond type information.

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 specific verb ('Generate') and resource ('creative or long-form content'), and explicitly states the routing mechanism ('routes to the best generation model'). This clearly distinguishes it from siblings like llm_analyze, llm_code, or llm_classify by focusing on generative content creation.

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 'Best for' section explicitly lists appropriate use cases (writing, summarization, brainstorming, content creation). However, it lacks explicit negative guidance or named alternatives (e.g., 'use llm_code for programming tasks'), though the content-type focus implicitly steers users away from analytical siblings.

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