update_intent

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

Description explicitly calls out the mutating nature and dryRun behavior, adding value beyond annotations. Annotations already indicate idempotency and non-destructive nature, but description provides actionable context (validate before updating).

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?

Three sentences: purpose, mutation+dryRun hint, post-update action. Every sentence is necessary and efficient, 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?

Given 12 parameters and no output schema, the description covers the key behavioral aspects: mutation, validation via dryRun, and retraining requirement. It does not specify return values, but that is acceptable without an output schema.

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 has 100% description coverage for all 12 parameters. The description adds marginal value by highlighting dryRun=true (default) and its validation purpose, but most param semantics are already clear from the schema.

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 'Updates an existing intent in a Cognigy.AI flow,' which is a specific verb+resource combination. It distinguishes from sibling tools like create_intent and delete_intent.

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?

Provides explicit guidance: use dryRun=true (default) to validate first, and after updating call train_intents to retrain the NLU model. This helps the agent understand safe usage and follow-up actions.

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