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

Annotations already declare readOnlyHint=true, so the description doesn't need to repeat that. The description adds valuable behavioral context: the tool is free and uncapped, the status transitions are forward-only (a later flagged submission never demotes a suggestion), and the distinction between one suggestion status and N per-destination receipts. This goes beyond the annotation and helps the agent understand the tool's semantics. The only minor gap is not describing the exact response structure, but since there's no output schema, the description does a good job of explaining what will be returned (status, draft, gate receipt with verdict, per-check detail, thresholds).

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 dense but well-structured. It front-loads the core purpose and the key usage directive ('always use this instead of lint_draft'), then explains parameter behavior, then the ownership model. Every sentence adds value. It's slightly long but justified given the complexity of the status model. The structure is logical: purpose, usage, parameter semantics, and model explanation.

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 complexity (one status vs N receipts, forward-only transitions, per-destination details), the description is remarkably complete. It explains what the tool returns (status, draft, gate receipt with verdict, per-check detail, thresholds), when to use it, and the underlying model. There's no output schema, so the description carries the full burden, and it does so thoroughly. An agent could call this tool correctly without additional information.

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 compensate. It does: it explains the `format` parameter's semantics (pass it to read one destination including stored draft text; omit for overview without draft bodies) and implies `suggestionId` is the identifier for the suggestion. The description adds meaning beyond the schema's bare type definitions. However, it doesn't explicitly describe the `suggestionId` parameter beyond the context, but the name is self-explanatory. Given the 0% coverage, this is a strong compensation.

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 purpose: to re-read an article suggestion's verification state without re-linting, providing the pipeline status and per-destination draft and gate receipt. It distinguishes itself from lint_draft by explicitly saying to use this instead of lint_draft to recover a known verdict. The verb 're-read' and resource 'article suggestion's verification state' are specific and unambiguous.

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 description provides explicit usage guidance: 'always use this instead of lint_draft to recover a known verdict' and explains when to pass `format` (to read one destination including stored draft text) versus omit it (for per-destination overview without draft bodies). It also explains the status-ownership model, which helps the agent understand when to use this tool versus alternatives. This is exemplary guidance.

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