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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds valuable context: it mentions the tool returns source coverage and failures, and that one missing record does not hide others. It also clarifies the tool is an analytical aid, not a substitute for direct verification, which is useful behavioral context beyond the annotations.

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 a single, well-structured paragraph that front-loads the core purpose, then details the output, usage context, and caveats. Every sentence adds value: the batch limit, output components, export formats, use case, independence of evaluations, and the analytical-aid disclaimer. No wasted words.

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 moderate complexity (batch processing, multiple output formats, independent evaluations), the description covers all essential aspects: input constraints, output structure, export options, use case, and limitations. The annotations provide the safety profile, and the schema covers parameters, so the description is complete for an agent to select and 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 100%, so the schema already documents both parameters. The description adds the batch limit (up to 10) and the accepted comma-separated string format for dot_numbers, which is helpful for agent convenience. It also mentions the default format (markdown) and the available export formats, reinforcing the enum values.

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 vets a batch of up to 10 US motor carriers by USDOT number, returns one GO/CAUTION/NO-GO result per carrier, and lists the output components. It distinguishes itself from siblings like carrier_vetting_score and carrier_vetting_evidence_pack by focusing on batch processing and the specific output format.

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 explicitly states when to use this tool: 'Use this to screen a broker's candidate list or lane roster.' It also explains the batch behavior (each carrier evaluated independently) and notes it is an analytical aid, not a substitute for direct FMCSA verification, which helps the agent decide when not to rely on it.

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