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

Annotations already provide readOnlyHint=false, idempotentHint=false, and destructiveHint=false, but the description adds meaningful behavioral context: the set is scoped to exactly one entity, belongs to the evaluations group, and participates in a multi-step iteration workflow. The instruction to follow llmContext guidance also signals behavior that is not encoded in the schema. It could go slightly deeper into side effects or lifecycle expectations, but this is strong coverage.

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 longer than average but every section earns its place: definition, iteration loop, onboarding pointer, and result-handling instruction. It is front-loaded with the core definition and then layers workflow context. A little trimming could improve scannability, but overall it is well-structured and not redundant.

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's complexity, the description is complete: it explains the tool's role in the evaluation workflow, documents prerequisites and next steps, and relies on a fully described schema and output schema for parameter and return details. An agent has everything needed to select and invoke the tool correctly.

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 description coverage is 100%, so the schema already documents all parameters well. The description adds the meaningful scoping nuance that entityId must identify a single extractor, classifier, or splitter, but it mostly relies on the schema's rich parameter descriptions. This meets the baseline for fully covered schema semantics.

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 a specific verb and resource: 'Create an evaluation set — a named collection of ground-truth examples scoped to ONE extractor, classifier, or splitter via entityId (evaluations group).' This precisely distinguishes the tool from related tools like add_evaluation_items, list_evaluation_sets, and run_evaluation. The resource scope and group context are immediately clear.

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 explicitly lays out the full iteration loop: create a set → add items with add_evaluation_items → publish a new version → run_evaluation → read metrics with get_evaluation_run. It also directs new users to call get_documentation first with a specific URL. This gives an agent clear when-to-use and next-step guidance beyond what the schema conveys.

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