invariance_eval_scorer_create

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

Annotations indicate readOnlyHint=false and destructiveHint=false, meaning this is a creation operation with no destructive side effects, and the description aligns by stating 'Register'. It adds some context about the scoring rule's behavior (0..1 score) but does not disclose potential errors (e.g., duplicate names), validation requirements, or response format. Given the minimal annotations, the description could provide more behavioral detail.

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 sentence that front-loads the verb and resource, includes a parenthetical definition, and lists the built-in kinds without redundancy. It is concise and every part contributes to understanding the tool's purpose and usage.

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 has only one parameter but that parameter is a complex JSON string, the description covers the essential aspects: the purpose, required fields, optional config, and built-in kinds. However, it does not specify what the response contains (e.g., the created scorer object or ID) since there is no output schema, nor does it address uniqueness constraints or error scenarios, which would improve completeness for a creation 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 schema already describes the 'body' parameter as a JSON string with required fields name and kind, and optional config, achieving 100% coverage. The tool description adds value by enumerating the built-in kinds (exact_match, contains, etc.) and providing an example config, which enriches the schema's otherwise generic description and helps the agent construct valid requests.

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 registers a scorer and defines what a scorer is (a named scoring rule mapping output+expected to a 0..1 score). It also lists built-in scorer kinds, distinguishing it from sibling list tools like invariance_eval_scorer_list and invariance_eval_scorers_list_builtin, which only retrieve existing scorers.

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 provides clear context by explaining the tool's purpose and the available built-in scorer kinds, implying when it should be used (to create a new scorer). However, it does not explicitly mention alternatives or conditions for when not to use it, such as checking existing scorers first or using list tools, which would strengthen guidance.

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