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

No annotations are provided, so the description carries the behavioral disclosure burden. It states that messages are sent to the model and a JSON response is returned, which covers the basic operation. However, it omits details like authentication needs, rate limits, streaming behavior, or error semantics, leaving gaps for a 29-parameter API.

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 well organized with a purpose statement, use-case bullets, and a returns section. However, some content is redundant: the opening sentence and second sentence overlap, the model list repeats the schema enum, and the first 'Use this when' bullet restates the core purpose.

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 large parameter count, the schema fully documents parameters and an output schema exists, so the description does not need to explain return values in depth. It provides purpose, model scope, and usage scenarios. It could be more complete on operational constraints, but it covers the main decisions an agent needs to make.

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?

Schema description coverage is 100%, so every parameter already has documentation. The description adds the model list, but that duplicates the model enum in the schema. It does not meaningfully enrich parameter understanding beyond what the schema already provides, so baseline 3 is appropriate.

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 opens with a specific verb and resource: 'Create a GLM chat completion using the AceDataCloud GLM API.' It then explains the core action (sending messages and returning generated responses) and clearly separates this tool from siblings like glm_list_models 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 'Use this when' section gives concrete triggers: chatting with Zhipu GLM, Chinese language tasks, and reasoning capabilities. It does not explicitly name sibling alternatives or exclusion criteria, but the context is clear enough to guide selection.

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

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

With no annotations provided, the description carries the burden of behavioral disclosure. It states 'Returns: Complete usage guide for GLM tools,' which indicates the output, but it does not mention side effects, authentication requirements, or whether the guide is static or dynamically generated. This is minimally adequate for a read-only documentation tool.

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 somewhat repetitive: the first sentence says 'Get a comprehensive guide,' the second says 'Provides detailed information,' and the third repeats 'Complete usage guide.' It is front-loaded and short, but the redundancy means not every sentence 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?

For a zero-parameter tool with an output schema, the description is sufficiently complete. It states the purpose and the return value. It could add guidance on when to call it relative to other GLM tools, but this is not essential given the tool's simplicity.

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, so the baseline is 4. The description mentions 'parameters' as part of the guide's content, but this is not a parameter-semantics issue for the tool itself. No further explanation is needed.

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 uses a specific verb+resource: 'Get a comprehensive guide for using the GLM tools,' and further details the content ('parameters, examples, and best practices'). This clearly distinguishes it from siblings like glm_list_models and glm_chat_completions.

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 makes the tool's purpose evident: it is for obtaining a guide to using GLM tools effectively. However, it does not explicitly state when not to use it or mention alternatives, though the sibling tools are clearly different in function.

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

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.