glm_find_bugs

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

No annotations are provided, so the description must disclose behavior. It states the return format and mentions 'using GLM' (implying an external model call), but it does not disclose side effects, data handling, permission requirements, or the fact that this is a non-destructive analysis. This leaves behavioral expectations underspecified.

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-structured with purpose, usage, args, and returns. It avoids filler and is easy to scan. The bullet-style sections earn their 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?

Given the tool's simplicity and presence of an output schema, the description covers the core purpose, parameters, return format, and usage context. It does not cover edge cases like error behavior or file path resolution details, but these are not critical for basic invocation.

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 0%, so the description must define parameters. It does: code_or_file, working_directory, and model are all explained, including the two model options. It could mention defaults and the optional nature of working_directory, but it goes beyond the raw 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 a clear verb and resource ('Find potential bugs in code') and reinforces with specific use cases ('bug detection, edge case analysis, error prone patterns'), distinguishing it from sibling tools like glm_explain or glm_review.

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?

Provides explicit 'Use for' list that defines when to apply the tool, but does not explicitly mention when not to use it or name alternative tools. The use case list is clear enough to guide selection among siblings.

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