bulk_label_issues

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

With no annotations, the description carries full burden. It mentions dry-run mode default, which is useful, but fails to disclose that this tool modifies issues (when not dry-run), potential irreversibility, rate limits, or required permissions. Critical behavioral aspects are omitted.

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 extremely concise: one sentence plus a note about default. Every word is functional, and the key action is front-loaded.

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 bulk operation affecting multiple issues, the description is too sparse. It lacks explanation of the output, error scenarios, or safety warnings. Even with an output schema, the context for safe and effective use is incomplete.

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 has 0% description coverage; description only adds meaning for dry_run (default) and filter_text (implicitly via 'matching a text filter'). Other parameters (owner, repo, label) are left to their names. The description adds marginal value over the 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 clearly states the tool applies a label to issues matching a text filter. It uses a specific verb ('apply') and resource ('label to issues'). However, it does not explicitly differentiate from sibling tools like triage_issues or close_resolved_issues, but the bulk text-filtering nature is distinct.

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?

No guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. The description only notes the default dry-run mode, which is behavioral rather than usage context.

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