train_intents

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

Annotations already indicate readOnlyHint=false and destructiveHint=false. The description adds that training is mutating and async with polling, which provides valuable behavioral context beyond the annotations. It warns about the mutating nature, helping the agent understand side effects.

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 only three sentences, front-loaded with the core purpose. Every sentence adds value: 'Trains the NLU model' (purpose), 'MUTATING: ...' (behavior), 'Use dryRun=true...' (guidance), 'Training is async...' (behavior). No extraneous 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?

Given no output schema, the description covers essential aspects: purpose, mutating effect, dry-run recommendation, and async polling. It does not detail the return structure, but the polling behavior is explained. The tool is moderately complex with 6 params, and the description is sufficiently complete for correct invocation.

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 coverage is 100%, so all parameters are documented. The description emphasizes using dryRun for validation, which reinforces the schema's default. However, it does not add new semantic information beyond what the schema already provides, so the baseline score of 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 the tool trains the NLU model for a Cognigy.AI flow, using the verb 'trains' and specifying 'NLU model' and 'flow'. This distinct purpose sets it apart from sibling tools like 'run_regression' or 'audit_nlu', which have different objectives.

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 advises using dryRun=true (the default) to validate before actual training, which is a clear usage guideline. It does not explicitly list alternatives or when not to use, but the tool is unique among siblings for training, so guidance is adequate.

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