list_models

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only states the listing action without describing any side effects, permissions, or constraints (e.g., it is read-only, no destructive potential). The description lacks transparency about the tool's behavior beyond the basic action.

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 with no redundancy. The main action is front-loaded, and the optional filtering is explained concisely with examples. Every word contributes value.

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 simple parameter set and the presence of an output schema, the description is complete. It covers the tool's purpose, filtering, and examples. No missing information is evident.

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?

The sole parameter 'name_filter' is described in detail: a case-insensitive substring filter against model name or label, with concrete examples. Since schema coverage is 0%, the description fully compensates by adding meaning beyond the schema, which only defines type and default.

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 it lists Odoo models (technical name + label) with optional filtering. The verb 'list' plus the specific resource 'Odoo models' makes the purpose unambiguous, and it distinguishes from sibling tools that deal with records or other entities.

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 explains the optional filter with examples, which implies usage, but does not explicitly state when to use this tool versus alternatives like search_read or read_records. No when-not-to-use or prerequisites are mentioned.

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