list_models

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

Annotations declare readOnlyHint=true, so the description does not need to reiterate non-destructiveness. However, the description does not detail return format or behavior beyond what is implied by parameter semantics. It could mention that it returns a list of model records.

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 extremely concise: one sentence for purpose followed by a structured Args section. No unnecessary words, and the most important information (what it does) is front-loaded.

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 list tool with 3 parameters and an existing output schema (not shown but present), the description covers the essential behavioral details (filtering, pagination). It is sufficient for an AI agent to invoke correctly, though it could briefly mention the return type.

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?

Although the schema has 0% description coverage, the tool description provides clear, actionable explanations for each parameter (domain, limit, offset) including defaults and examples, fully compensating for the schema gap.

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 'List installed Odoo models', using a specific verb and resource. It distinguishes from sibling tools like read_record (reads a single record) and search_records (searches records, not models).

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?

No guidance is provided on when to use this tool versus alternatives. No exclusions or context for when not to use it. Given the sibling tools list, explicit differentiation would help an AI agent select the correct tool.

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