review_document

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

With no annotations, the description fully covers behavior: multi-model fan-out, knowledge retrieval, cost control limits, model-specific effort handling, and caching (cache_hit/reuse_count). Exceptionally transparent.

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?

Structured with a summary, technical process, return info, and bulleted Args. Each sentence adds value; no redundancy. Front-loaded with the core 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 complexity (12 params, no output schema), the description covers return structure, cache, budget enforcement, and model-specific behavior. Virtually no gaps for an AI agent to understand correct invocation.

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%, but the description includes a detailed Args section explaining every parameter's purpose, defaults, constraints (e.g., effort affects only specific models, max_cost_usd triggers budget trimming). Fully compensates for missing schema descriptions.

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 reviews documents (markdown/code/adr/rfc/config) with multi-model AI and knowledge retrieval. The verb 'review' and specific resource types make the purpose explicit.

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?

No explicit guidance on when to use this tool over siblings like review_code or review_plan. The description focuses on internal mechanics rather than usage context.

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