llm_generate

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

Discloses that complexity drives model selection (cheap vs premium). No annotations exist, so description carries burden; it could add more on side effects or idempotency but generation tool is inherently non-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?

Reasonably concise with clear parameter descriptions. Slightly verbose in parameter list but still efficient. Could benefit from bullet-point structure.

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?

Output schema exists so return values aren't needed. Tool has 6 params with one required; complexity highlighted. Could mention output type (text) but not critical. Fairly complete for its complexity.

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?

With 0% schema coverage, description fully compensates by explaining each parameter: prompt, complexity (with examples), system_prompt, temperature, max_tokens, context. Adds meaning beyond raw 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?

Clearly states 'Generate creative or long-form content' with examples (writing, summarization, brainstorming, content creation). This distinguishes it from siblings like llm_analyze or llm_code.

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 'Best for' list indicating usage context. Does not explicitly state when not to use or name alternatives, but the guidance is clear enough for selection among many generation-related siblings.

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