ai.triage_issue

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

No annotations are provided, so the description bears the burden of behavioral disclosure. It reveals the outputs (classification and explanation) but does not mention whether the tool modifies anything, how decisions are made, or confidence levels. This is acceptable for a read-only classifier but lacks depth.

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 a single sentence, front-loaded with the action ('Classify an issue') and concise, covering the main purpose and outputs without unnecessary filler.

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 tool with three simple parameters and an output schema, the description covers the core function and outputs. However, it lacks explicit usage guidance and parameter semantics, which are important for an AI agent to invoke it correctly in all contexts.

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%, and the description adds no parameter details. Although parameter names (owner, repo, issue_number) are self-explanatory, the description does not elaborate on their roles, constraints, or how they relate to the classification task.

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 clearly states the tool's function: classifying an issue and specifying outputs (type, priority, suggested labels) and explanation. This distinguishes it from sibling tools like ai.summarize_issue, which focus on summarization rather than classification.

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?

Usage guidance is implied by the verb 'classify' and the issue context, but the description does not explicitly state when to use it versus alternatives like ai.summarize_issue or github.get_issue. No exclusions or alternative calls are mentioned.

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