invariance_eval_dataset_seed_suite

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

Annotations indicate readOnlyHint=false (mutation), destructiveHint=false, and openWorldHint=true. The description adds that it can create a dataset, append rows, create a suite, create cases, and optionally start an eval run (side effects). It doesn't mention any destructive actions or irreversible changes, which aligns with the annotations. It could add more detail about whether the run is started synchronously or async, but the basics are covered.

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 very short (two sentences) and front-loaded with the key action ('One-call eval setup'). Every sentence adds value: the first states the scope and steps, the second positions it as the preferred MCP path. 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?

For a tool with a single parameter that has 100% schema coverage and a rich example embedded in the schema, the description is sufficient for an agent to understand the tool's purpose and main flow. It could mention what happens if run=true (side effects), but the schema's example covers the format. The output is not defined, but the tool produces side effects rather than returns, which is acceptable. Overall, it's nearly complete.

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 description coverage is 100% and the schema already describes the body parameter in detail, including required and optional fields with an example. The description adds minimal extra meaning beyond what the schema provides, just emphasizing the one-call orchestration. Thus, baseline 3 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 clearly states a specific verb ('One-call eval setup') and resource ('dataset, suite, cases, run'), and distinguishes it as the preferred MCP path for turning JSON examples into runnable evals, which differentiates it from the many sibling tools like invariance_eval_dataset_create and invariance_eval_suite_create.

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 frames itself as the 'preferred MCP path' for converting JSON examples into runnable evals, giving clear context. It also implies when to use it over manual multi-step tools (create dataset, append rows, etc.), though it doesn't name alternatives explicitly. Still, the guidance is strong and actionable for an agent.

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