candidate_submit

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

No annotations provided, so the description carries full burden. It discloses that the tool is mutating ('adds an item to the review queue') and explains the review process. However, it does not mention potential side effects like queue limits or validation behavior beyond the dry_run parameter.

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?

Two sentences: first explains the action and its mutational nature, second provides usage guidance and alternative tool. Front-loaded, no wasted words.

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 complexity of 16 parameters and no output schema, the description adequately explains the core function but does not specify return values or behavior when optional parameters are omitted. The 100% schema coverage partially compensates, but the missing output schema leaves some ambiguity.

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 coverage is 100% with descriptions for all 16 parameters, so baseline is 3. The description does not add additional meaning to individual parameters beyond what the schema already provides.

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 queues a proposed memory for human review, using specific verbs like 'Queue' and 'adds an item to the review queue'. It directly contrasts with the sibling `remember` tool, which writes directly to recall.

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?

Explicit guidance: use for 'plausible-but-unverified inferences' versus `remember` for 'confirmed facts'. Also notes that memories enter recall only after human approval via CLI/dashboard, providing clear when-to-use and when-not-to-use context.

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