ypollak2/llm-router

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

No annotations provided, so description carries full burden. Discloses caching logic, conditional return types (cached data vs Playwright JS snippet), execution context ('no page navigation needed'), and side effects on model routing. Lacks only potential error states or rate limits.

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?

Three tightly focused paragraphs: core purpose, caching/implementation details, and routing implications. No redundant text; every sentence provides actionable information. Well front-loaded with the primary function.

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 output schema exists, the description adequately covers return value semantics (data vs snippet) and operational context (routing impact). Complete for a zero-parameter tool, though explicit error handling mention would perfect it.

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?

Zero parameters present (empty schema), establishing baseline 4. Description appropriately focuses on return behavior and side effects rather than non-existent parameters.

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?

States specific action ('Check') and resource ('real-time Claude subscription usage') with precise scope ('session limits, weekly limits, extra spend'). The mention of 'real-time' and caching behavior distinguishes it from siblings like 'llm_usage' or 'llm_refresh_claude_usage'.

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?

Explains operational behavior (cached data vs JS snippet return) and crucial downstream impact ('budget pressure feeds directly into model routing'). However, it does not explicitly name sibling alternatives or state when to prefer this over 'llm_usage' or 'llm_refresh_claude_usage'.

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