glm_explain

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

With no annotations provided, the description carries the transparency burden. It clarifies the return value ('Returns: Explanation') and that it's an explanation task, but doesn't disclose operational details like model cost, latency, or that the model choices ('haiku', 'sonnet') have different capabilities.

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 front-loaded with the primary purpose, followed by a bulleted usage list, an Args block with clear annotations, and a Returns line. Every sentence delivers value with no waste.

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 simple 3-parameter explain tool with an output schema, the description covers the purpose, usage context, all parameters, and return type. It's sufficiently complete for an agent to select and invoke it correctly.

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 0%, so the description fully compensates by giving meaningful semantics for each argument: code_or_concept, context, and model with allowed values. Without this, the agent would only have parameter names and types.

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 clear verb+resource statement: 'Explain code or a concept using GLM.' It also lists use cases (understanding code, learning concepts, documentation) that distinguish it from 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?

The 'Use for' section provides clear context for when to use it (understanding, learning, documentation), but it doesn't explicitly name alternatives or when-not to use it, so it's a step below the highest bar.

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