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

Beyond the readOnlyHint=true annotation, the description discloses specific error behavior ('an unknown slug errors with the valid list'), rate limiting with 429 handling and retry instructions, the response envelope (result, confidence, _source, citation), and provenance status. This goes well beyond the annotation's simple safety signal and sets expectations accurately.

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 structured into clear paragraphs (overview, usage, behavior, returns, limitations, related) with no redundant fluff. Each sentence adds distinct information: scope, fields, filtering, error handling, rate limits, response shape, caveats, and cross-references. It is long but every part earns its place.

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 tool has an output schema, but the description still explains the response envelope and provenance_status, and it notes limitations about typical specs and legal payload. Given the simple 3-optional-param API, the description covers all necessary context: what, how, when, errors, rate limits, and relationships to sibling tools.

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%, and the description adds usage semantics on top: omitting slug lists all records, while category and region filter the list. It gives a concrete slug example and enumerates allowed enum values, making parameter behavior intuitive beyond the schema's short descriptions.

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 'Look up road-freight vehicle and trailer specifications' — a specific verb and resource — then enumerates 17 vehicle types and the data fields each record carries. It is clearly differentiated from sibling lookup tools (e.g., container_lookup, uld_lookup) by focusing on road-freight equipment and even names related calculators as alternatives.

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?

It explicitly explains how to call it: 'Provide slug... omit it to list all 17; category... and region... filter the list.' It also provides contextual alternatives under 'Related', such as 'ldm_calculator (whether a pallet load fits)', signaling when to choose another tool. It mentions get_subscribe_link for rate-limit mitigation, giving a clear path for when this tool's limits are insufficient.

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