autofill_emit

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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It explains that the tool emits a JSON prompt and describes the return structure, but does not discuss side effects, safety, or idempotency. While likely non-destructive, more explicit behavioral context could improve transparency.

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 well-structured: it begins with the core purpose, followed by usage guidelines, arguments, return values, and an example. Every sentence is concise and adds value, with no redundancy.

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 that an output schema is present, the description effectively covers the return structure (prompt, paper_count, error) and provides an example. With only one simple parameter, the description is complete and sufficient for an agent to use the tool 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?

With 0% schema description coverage, the description compensates by explaining the parameter 'cluster_slug' as the cluster whose notes are scanned for TODO placeholders. This adds meaningful context beyond the schema's type definition.

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 verb ('Build an autofill prompt') and the resource ('paper notes with TODO bodies'). It also explicitly distinguishes the tool from its sibling 'autofill_apply' by noting when to use each.

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 conditions for use ('after ingest creates notes with abstracts but TODO content') and non-use ('to write AI output; use autofill_apply instead'). This gives clear guidance on when the tool is appropriate and when to choose an alternative.

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