list_models

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It states 'Get all' but includes a limit parameter, which is contradictory. It does not mention read-only nature, pagination, or ordering behavior, leaving important behavioral traits unclear.

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?

Description is concise and front-loaded with the main purpose. It uses structured sections (Args, Returns) which aid readability. However, the sections add redundancy since the schema already defines the parameter and output.

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?

The tool is simple with one parameter and an output schema, so the description is mostly adequate. However, it lacks context on what 'configured' means, potential prerequisites, or how it relates to user-specific models. The output schema likely covers return values, but missing broader usage context.

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 description adds value by specifying 'limit: Maximum number of results (1-100)', which clarifies the range beyond the schema's default. Schema coverage is 0%, so this manual description compensates well for the only parameter.

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 explicitly states 'Get all configured AI models' with a clear verb and resource. It distinguishes itself from siblings like 'get_model' (singular) and 'get_default_models' (specific subset).

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 on when to use 'list_models' versus alternatives like 'get_default_models' or 'get_model'. The description does not mention contexts or exclusions, leaving the agent to infer usage from the tool name alone.

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