data_quality

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

With no annotations, the description carries the burden of behavioral disclosure. It details the varying behavior for each info_type, parameter dependencies, and optional pagination/sorting. It implies a read-only query operation, which is appropriate for the tool's purpose. While it does not mention authentication or rate limits, the description provides substantive behavioral context beyond mere schema fields.

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 well-structured with a brief purpose statement, a clear bulleted list of info_type options, and a separate args list. It is appropriately sized for the tool's complexity, with every line providing actionable information and no redundant filler.

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 (four modes, nine parameters) and lack of output schema/annotations, the description is largely complete. It covers all input semantics and mode-specific requirements. However, it does not describe the return value or response structure, which would be useful but is not critical for a query tool. The description overall is strong and sufficient for an agent to invoke the tool 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 description coverage is 0%, so the description must fully compensate. It does so by documenting all nine parameters, including their purpose and required formats (e.g., 'start: Start datetime in RFC-3339 format', 'platform_id: "ad" or "azure"'). This is a complete and high-quality parameter explanation, exceeding what the minimal schema provides.

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 opens with a specific verb-resource pair: 'Query data quality and collection statistics from BloodHound.' It then enumerates distinct info_type variants (completeness, ad_domain, azure_tenant, platform), which clearly delineates the tool's scope and differentiates each mode. This is specific and distinguishes from sibling tools like collection_info or risk_posture.

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 explicit usage guidance for each info_type, including which parameters are required for each (e.g., ad_domain needs domain_id, azure_tenant needs tenant_id). It does not explicitly mention alternatives or when not to use this tool, but the provided context strongly implies the appropriate use cases, making it clear enough for an agent to select this tool correctly.

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