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

Description goes beyond the readOnlyHint annotation by detailing deterministic behavior, what it returns (slugs, tier, etc.), and what it never does (compute or guess). This provides rich behavioral context not covered by 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 well-structured with a clear first sentence, then clarifying details, next steps, and an example. It is informative without being overly verbose, though a slight reduction would be possible.

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 output schema exists, the description covers the key return elements. It addresses ambiguity and next steps, and mentions the alias. It is complete for the tool's purpose, though could briefly note error cases.

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 has 0% description coverage, so the description must compensate. It implies the 'description' param meaning and gives an example for 'preferred_standard', but the 'return_resolution_log' param is not mentioned at all, leaving a coverage gap.

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 it resolves a plain-English description to candidate assemblies and fields. It explicitly distinguishes from compute tools by stating it does not return a Bill of Quantities or guess values, making the purpose unambiguous.

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 tells when to use: to get candidate slugs and fields from a description. It then instructs to call compute_<slug> next. It does not explicitly list when not to use, but the context is clear enough given the sibling compute tools.

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