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

With no annotations, the description carries the burden. It discloses that it uses glm-5.2 and that other models are paid, which is helpful cost behavior. It doesn't mention side effects or output format, but as a prompt builder it's likely safe. Still, it could disclose that the result is a prompt text or that it doesn't execute anything.

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 concise (three sentences) and front-loaded with the primary function. It includes usage condition and cost warning, all relevant. No wasted words.

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 no output schema and no annotations, the description should explain what the tool returns and any side effects. It doesn't mention that the result is a prompt string, nor how to handle errors. It's adequate for a simple tool but lacks detail about the return value.

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 coverage is 100%, so all parameters have descriptions in the schema. The tool description adds no additional meaning to the parameters; it only repeats the general purpose. Baseline 3 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 states a specific action: composing a prompt for a specific tool using the prompt-master skill, and specifies the model (glm-5.2). It clearly distinguishes this from running a prompt (sibling tools like glm_run). However, it doesn't explicitly name an alternative tool, so it's clear but not maximally differentiated.

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?

It provides a usage condition: 'for cases when the orchestrator does not have its own skill', and adds a warning about not substituting paid models unnecessarily. This gives context for when to use it, though it doesn't name an explicit alternative tool to use instead.

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