ingest_outline

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

With no annotations, description fully informs about the tool's behavior: validation, injection, saving, and the return format. Also notes the required user confirmation step, providing complete transparency for a non-destructive data ingestion.

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?

Description is concise, well-structured with clear sections for Args and Returns, and uses bold for important warnings. Every sentence adds value without 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 the tool's simplicity (2 params) and presence of output schema, the description provides all necessary context: workflow ordering, user interaction requirement, and error handling via re-call. No gaps.

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?

Despite 0% schema description coverage, the description provides detailed explanations for both parameters, including source (project_id from prepare_outline) and expected format (outline_json matching a specific schema).

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?

Description clearly states the tool's action: validates, injects presenter info, and saves outline JSON. It differentiates from siblings like prepare_outline by specifying it is called after preparation.

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?

Explicitly instructs to call after prepare_outline, highlights the need to show outline to user for confirmation before proceeding, and explains how to handle user requests for changes by calling again.

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