glm_list_models

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

With no annotations, the description must carry behavioral disclosure. It indicates the tool returns a formatted list, which implies read-only behavior, but it does not explicitly mention safety, side effects, or any limitations. This is adequate for a simple listing tool but lacks depth.

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 short but contains redundancy, repeating the return information in the prose and the 'Returns:' line. It is not as sparse as ideal, but it is still generally concise and 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 the tool's complexity, the description is fairly complete. It states what it does and what it returns, and since an output schema exists, it does not need to detail return values further. It lacks usage guidelines, but that is covered separately.

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 schema coverage is 100% (empty schema). The baseline for 0 params is 4, and the description does not need to explain parameters. It adds no parameter details because none exist.

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 all available GLM models for the GLM API' with a specific verb-resource combination. It also adds that models come with descriptions, distinguishing it from sibling tools like glm_chat_completions and glm_get_usage_guide.

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 no guidance on when to use this tool versus alternatives. It does not mention any specific use cases, exclusions, or sibling tools, leaving the agent to infer usage solely from the name and basic purpose.

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