Список моделей

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states that models come from the configured endpoint; it does not describe whether the call is live, cached, ordered, or what the return shape looks like. This is minimal behavioral context.

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 a single short sentence with no filler. It front-loads the core subject and adds the useful source detail about the OpenAI-compatible endpoint. Every word earns its place.

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 low in complexity and requires no parameters, but there is no output schema and no description of the return format. An agent trying to pick a model for glm_run may need to know whether the result is a list of IDs, names, or objects; that is currently unspecified.

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 tool has zero parameters and the input schema is an empty object, so schema coverage is effectively 100%. The description adds no parameter details, but none are needed; with no parameters, the baseline of 4 applies.

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 identifies the tool as returning the models provided by the configured OpenAI-compatible endpoint, and the title 'Список моделей' reinforces it as a listing operation. It is clearly distinct from siblings like glm_run or glm_apply. It lacks an explicit imperative verb, but 'returns' plus the title makes the purpose clear.

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 given on when to use this tool versus alternatives. It does not mention related tools, preconditions, or any scenario where this list should be consulted, so an agent receives no help in selecting it over other GLM tools.

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