llm_analyze

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It explains model routing based on complexity parameter but omits important details such as cost implications, rate limits, or potential side effects (e.g., high token usage). The guidance on complexity levels is helpful but incomplete.

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 extremely concise: a one-line purpose statement followed by a bulleted list of parameters. Every sentence adds value, and the structure is front-loaded with the most important information. No redundant or irrelevant content.

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 tool's complexity (5 parameters, output schema exists), the description covers purpose, typical use cases, and parameter details. However, it lacks guidance on error conditions or how the output schema is structured. The presence of an output schema partially mitigates this, but additional context on expected results would improve completeness.

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?

The input schema has 0% description coverage, so the parameter descriptions in the tool description add significant value. Each parameter is explained with usage notes (e.g., complexity defaulting to moderate, complex for multi-file reviews). This compensates for the schema gap, though further detail on system_prompt and context could enhance clarity.

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 explicitly states 'Deep analysis task — routes to the strongest reasoning model' and lists specific uses like data analysis and code review. This makes the purpose clear but does not contrast with sibling tools like llm_classify or llm_auto, leaving some ambiguity about when to choose this over others.

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 description includes a 'Best for' section that implies appropriate use cases. However, it does not provide explicit guidance on when not to use the tool or mention alternative tools, leaving the agent to infer usage boundaries without clear exclusions.

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