Trait Vocabulary

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

Beyond the readOnly/idempotent annotations, the description adds meaningful behavioral context: 'Static reference data', 'Free L0, no authentication required', and the data limitation that certain traits are never returned about a third party. These details help the agent set expectations without contradicting 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 compact and front-loaded: it begins with the core purpose, packs the content scope into a parenthetical list, then gives usage timing and access characteristics. Every sentence carries useful information with no 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?

Even without an output schema, the description states what the response will cover (codes, categories, semantics, valid discovery contexts, third-party exclusions), when to use the tool, and access requirements. That is sufficient for a no-input reference tool.

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 tool accepts zero parameters, and schema coverage is 100%, so there is nothing for the description to add about parameters. The baseline of 4 for a no-parameter tool is appropriate.

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 uses a specific verb ('List') and resource ('canonical trait vocabulary'), then details the content: 30 trait codes, category names, one-line semantics per code, valid discovery contexts, and third-party exclusions. This distinguishes it clearly from sibling reference tools like list_archetypes and describe_competencies.

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 instructs the agent to use this before composing query_field trait_priorities or create_requirement trait_criteria, which is strong contextual guidance. It does not explicitly name alternative tools or when-not-to-use conditions, but the intended usage is clear enough.

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