query_commitment

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

With no annotations, the description must fully disclose behavior. It correctly indicates a read operation returning aggregated signals, but omits details like data freshness, verification method, potential rate limits, or error conditions. The claim 'verified' lacks elaboration.

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 consists of two clear, front-loaded sentences (35 words) with no filler. Every sentence provides essential information: query action, return values, and a qualitative remark about authenticity.

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?

For a simple single-parameter tool without output schema, the description adequately covers purpose, inputs, and outputs. However, adding data source or recency info would improve completeness. Sibling tools are distinct enough to not cause confusion.

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 sole parameter 'domain' has 100% schema coverage, and the description adds value by specifying normalization ('lowercase without protocol or path'). This extra context clarifies input processing beyond 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?

The description clearly states the tool queries 'verified behavioral commitment data for a domain' and lists exact return values (unique verified visitors, repeat visit rate, average time spent). It distinguishes from siblings by emphasizing 'real human engagement' as opposed to generic lookups.

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 implies the tool is useful for assessing authentic engagement ('harder to fake than reviews or content'), but lacks explicit guidance on when to use this tool versus alternatives (e.g., when to prefer audit_dependencies). No exclusions or prerequisites are mentioned.

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