Data Completeness Validation

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

No annotations are provided, so the description carries the full disclosure burden. It usefully discloses output size per mode (25 vs 200-400 tokens), which is genuine behavioral context, but says nothing about whether the operation is read-only, what it costs, or what happens on failed validation.

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?

Three compact sentences with the core action front-loaded. The emoji and the ALL-CAPS 'INTELLIGENT RESPONSES' header are marketing noise that costs a little signal, but nothing is padded or redundant.

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?

With no annotations and no output schema, the description must explain what validation produces and how the three parameters interact. It covers response_mode well but leaves domain and expected_asset_count semantics and the shape of the validation result unaddressed.

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%, so the baseline is 3, but the description adds real value beyond the schema by quantifying what each response_mode returns (25 tokens for minimal, 200-400 for standard, full audit for detailed). It leaves domain and expected_asset_count entirely to the 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?

States a specific verb and resource: 'Cross-validate tool results for accuracy and completeness.' An agent can tell this is a validation tool, though the phrase 'tool results' is vague about which inputs it consumes and nothing distinguishes it from siblings like analyze_security_risks or query_security_data.

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 explains the response_mode options but never says when to invoke this tool versus the eight sibling analysis tools, nor what prerequisite data must exist. The 'Use minimal/standard/detailed' lines are parameter guidance, not usage guidance.

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