ypollak2/llm-router

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

With no annotations provided, the description carries full behavioral disclosure burden. It explains the routing logic across providers and default duration, but omits critical behavioral traits typical for video generation: async/job-based processing, cost implications, or output file format details.

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?

Efficiently structured with the core purpose front-loaded, followed by compact parameter documentation. Every line provides value; no redundancy despite the Arg-style formatting necessitated by missing schema descriptions.

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 presence of an output schema (exempting return value documentation) and the parameter coverage, the description is reasonably complete. Minor gap: lacking mention of async behavior typical for video generation APIs, though this may be evident in the output schema structure.

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%, requiring the description to fully compensate. The Args section successfully documents all three parameters (prompt semantics, model examples with provider strings, duration defaults), providing necessary context absent from 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?

The description clearly states 'Generate a video' with specific video model references (Gemini Veo, Runway, Kling) that immediately distinguish it from sibling tools like llm_image or llm_audio. The routing behavior is explicitly mentioned.

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?

While the description implies usage through the specific video model names mentioned, it lacks explicit guidance on when to choose this over llm_image or llm_generate, or prerequisites like async handling expectations for video generation.

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