glm_summarize

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

No annotations are provided, so the description alone must disclose behavior. It states the tool summarizes text and returns the summarized result, which covers the core behavior. It does not discuss potential side effects, rate limits, or caveats, but the operation is inherently read-only and the return value is specified.

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 compact and well-structured: a one-sentence purpose, a use-for list, an args section, and a return statement. Every line adds necessary information.

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?

Despite having no annotations and a minimal schema, the description covers the tool's purpose, typical usage contexts, all three parameters with allowed values, and the return value. This is sufficient for a straightforward summarization tool, though it doesn't address edge cases like maximum input length.

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 input schema lacks descriptions for its properties, but the description compensates by listing each argument and its purpose, including explicit allowed values for style and model. This adds practical meaning beyond the bare schema.

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 'Summarize text using GLM,' which clearly states the verb and resource. It further lists specific use cases (document summaries, meeting notes, code explanations), distinguishing it from sibling tools like glm_explain or glm_analyze.

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 for' line provides clear contexts for when to apply the tool, covering document summaries, meeting notes, and code explanations. However, it does not explicitly mention when not to use it or reference alternatives, stopping short of full comparative guidance.

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