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 discloses the external routing behavior (ElevenLabs vs OpenAI), which is valuable context. However, it omits critical operational details: cost implications, output format (despite having an output schema), error handling for provider failures, and whether operations are idempotent or destructive.

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?

Front-loaded purpose statement followed by structured Args documentation. Given the schema coverage gap, every line serves a necessary function. Minor deduction for the abrupt 'Args:' formatting shift, which slightly breaks narrative flow but remains highly scannable.

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?

Adequate for basic invocation given the output schema handles return values, but incomplete for a dual-provider external API tool. Missing operational context such as authentication requirements, cost per request, audio format details, and timeout behavior that would help an agent handle failures gracefully.

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?

Excellent compensation for 0% schema description coverage. The Args section adds essential domain context: explains 'text' purpose, provides concrete model override examples with provider prefixes, and clarifies voice selection semantics (OpenAI enum values vs ElevenLabs voice IDs). This goes significantly beyond the bare type information in the schema.

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?

Clear specific verb ('Generate') + resource ('speech/audio') and explicitly distinguishes implementation ('routes to ElevenLabs or OpenAI TTS'). Among siblings like llm_generate, llm_image, and llm_video, this precisely positions the tool as the text-to-speech option.

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?

Provides no guidance on when to use this tool versus llm_generate or other siblings, nor when to choose ElevenLabs over OpenAI. No mention of prerequisites, costs, or rate limit considerations for external API usage.

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