llm_code

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

With no annotations, the description carries full burden. It explains model selection based on complexity, which is a key behavioral trait. It does not mention side effects, rate limits, or authentication, but for a simple LLM call, this is acceptable. No contradictions.

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 concise and well-structured: a one-liner, 'Best for' list, then parameter descriptions. Every sentence adds value, and it is front-loaded with the main purpose.

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, the description does not need to detailed return values. It covers all parameters and usage context. Minor gaps: no mention of error handling or cost implications, but overall complete enough for a tool that sends prompts to a model.

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 coverage is 0%, so the description must compensate. It explains all 5 parameters in the 'Args' section, adding meaning beyond schema titles, especially for complexity which drives model selection. However, it lacks details like validation constraints or token limits for max_tokens.

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 the tool routes to the best coding model for code generation, refactoring, and algorithm design. This differentiates it from sibling tools like llm_analyze or llm_edit, giving a specific verb and resource.

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' list and complexity explanation provide clear when-to-use guidance. However, it does not explicitly state when not to use this tool or mention alternatives like llm_generate or llm_edit, so it's slightly incomplete.

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