Data Completeness Validation

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool cross-validates tool results but does not mention effect on data, auth requirements, rate limits, or consequences of failure. This is insufficient for a mutation-like validation operation.

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 short (two sentences) but uses heavy formatting (emojis, caps) that adds noise. Key information on response modes is present but could be more succinct and structured, e.g., using bullet points.

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?

The description lacks explanation of what 'validation' entails, what return format to expect (no output schema), or how to interpret results. Given 3 parameters and no annotations, more detail on behavior and outputs is needed for an AI to invoke correctly.

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 100%, providing a baseline of 3. The description adds valuable context beyond the schema by specifying token counts for each response_mode (e.g., 'minimal' uses 25 tokens), enabling the AI to estimate cost and latency.

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 validates data completeness and accuracy, but it does not explicitly differentiate from sibling tools like 'analyze_security_risks' or 'discover_assets'. The name and title align well, so purpose is clear but sibling distinction is missing.

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 provides guidance on choosing response modes with token size hints, which helps the AI select appropriate verbosity. However, it lacks explicit 'when to use' vs. alternatives or any context about prerequisites or ordering relative to other tools.

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