glm_ask

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the lack of tool/file access, speed, cost, and return format. This is solid, though it omits details like rate limits or token caps, which are not critical for this simple 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 concise and well-structured, with a front-loaded summary followed by bullet-like usage notes. Every sentence serves a purpose, and the cost note is a useful extra without being verbose.

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 description covers purpose, parameters, and return value, and an output schema is present. It is complete for a simple two-parameter tool, though it could mention potential errors or rate limits, which are not essential.

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 description provides clear meaning for both parameters: 'question' is described as 'Your question or prompt', and 'model' is explained with allowed values and their trade-offs ('haiku' fastest, 'sonnet' better quality). The schema has no descriptions, so the description adds essential value.

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 'Quick question to GLM - no tools, fast response,' clearly stating the tool's purpose and scope. The 'Use for' list (explanations, analysis, brainstorming, quick answers) differentiates it from specialized siblings like glm_summarize and 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?

It explicitly lists use cases and states that the tool 'Does NOT have access to files or tools - pure generation', which tells when not to use it. However, it does not name specific sibling tools for specific tasks, so the guidance is clear but not exhaustive.

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