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

Since no annotations are provided, the description carries the full burden. It details the internal behavior: creates an actionStore entry with status='awaiting_approval', inline draft body, an executionMatrix pointer, and how the reviewer sees it in the Launch Room/IDE chat. It does not mention permission requirements or error handling, but the state-changing nature and approval flow are transparent enough for an AI agent to predict outcomes.

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 every sentence adds value: purpose, usage condition, internal state details, and loop context. It is front-loaded with the primary action, though the 'P75 —' prefix adds minimal noise. Slightly longer than strictly necessary but not wasteful.

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 has no output schema and no annotations, the description covers the essential context: when to use, what it creates internally, how the review process appears to users, and the broader loop. It does not explain exact return value or error cases, but the state-creation behavior is well specified. This is adequate for a non-trivial action.

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?

The schema already covers 100% of parameters, and the description adds contextual meaning beyond the schema. It explains that metrics should be the same object passed to chieflab_suggest_next_move, and that runId enables chain tracking. This extra semantic linkage helps the agent construct correct calls.

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+resource statement: 'turn a Next Move suggestion into an approval-gated draft action.' It explicitly distinguishes from siblings by referencing chieflab_suggest_next_move and specifying the condition (kind is not 'wait' or 'noop'). This is a specific purpose with clear scope.

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 usage guidance: 'USE WHEN you've called chieflab_suggest_next_move and the suggestion's kind is not wait or noop.' It also describes the workflow context (approval card flow) and the overall loop, making it clear when and why to invoke this tool versus alternatives like chieflab_approve_action.

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