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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, but the description adds substantial context beyond that: the suggestion heuristic, the confidence levels, the fallback when there is no clear majority, the fact that the dashboard never writes labels, and that the tool never fails but reports why it couldn't read the list. This is exactly the extra behavioral context an agent needs.

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 front-loads the core promise (what the list contains and what each row has) and then explains the logic, usage, and safety behavior. It is somewhat dense and the suggestion-algorithm sentence is long, but every clause carries substantive information rather than 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?

With no output schema, the description still describes the shape of each item (suggested area, the persona: label, a high/medium confidence, a one-line reason), the conditions under which a suggestion is absent, and the error behavior. That is complete enough for an agent to call this tool and interpret the results 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 description coverage is 100% and each of the three parameters (repo, limit, suggested_only) is documented in the schema. The description does not add meaning beyond the schema for these parameters — it doesn't, for example, explain how limit interacts with suggested_only or what happens when repo is omitted and multiple repositories are visible. Baseline 3 is appropriate.

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 names a specific resource (open issues with no area in a repository) and a specific output (a suggested area with confidence and reason) using precise domain criteria ('no [persona] title prefix and no persona: label'). It also defines what counts as 'no area' and what a suggestion is derived from, so an agent can tell this apart from generic listing or board tools.

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 states the triggering situation clearly ('when your human asks you to tidy up issues with no area') and spells out the intended workflow: show the list first, then have the human apply only agreed labels using their own GitHub access. It doesn't name explicit alternatives or exclusions, but there is no closely competing sibling tool, so the guidance is strong without being complete.

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