Update model AI instructions

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

The annotations already declare destructiveHint=true, so the agent knows this is a destructive operation. The description adds context by explaining the tool is for integrating user corrections into the hint, and that it should be called with the full updated hint. This goes beyond the annotations by clarifying the intended use case and the expected input format. However, it doesn't detail what exactly gets destroyed (the previous hint) or any irreversible consequences, but the annotation covers the destructive nature.

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 two sentences, front-loaded with the core purpose, and every sentence earns its place. The first sentence states what the tool does, the second explains when and how to use it. 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 the tool has only 2 parameters, an output schema, and annotations covering destructive behavior, the description is fairly complete. It explains the purpose, the trigger (user correction), and the expected input (full updated hint). The only minor gap is not explicitly stating what the output schema contains, but the output schema exists and the description needn't explain return values. The description is adequate for an agent 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 0%, so the description must compensate. The description mentions 'model' and 'updated_hint' implicitly: 'for an Odoo model' and 'call this tool with the full updated hint'. This adds some meaning beyond the bare schema, but it doesn't fully explain what 'model' refers to (e.g., the model name string) or what 'updated_hint' should contain beyond 'full updated hint'. The description partially compensates but leaves some ambiguity.

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's purpose: updating AI instructions (hint) for an Odoo model based on user feedback. It specifies the verb 'update', the resource 'AI instructions (hint) for an Odoo model', and the context 'based on user feedback'. This distinguishes it from sibling tools like aidoo_write or aidoo_create, which are generic CRUD operations.

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 explicit guidance on when to use this tool: 'When the user corrects a result or points out an error'. It also describes the workflow: reformulate the existing hint to integrate the correction, then call this tool with the full updated hint. This is clear and actionable, though it doesn't explicitly name alternatives, the context is specific enough to avoid confusion with siblings.

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