prepare_visual_qa_analysis

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

With no annotations, the description carries full burden. It states 'No LLM call' and describes the return structure, but does not disclose potential side effects or idempotency. The lack of destructive/readOnly hints makes the behavioral transparency adequate but not thorough.

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 concise and well-structured: a summary sentence, an important instruction in bold, and an Args section with bullet points. No fluff; every sentence adds value.

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 presence of an output schema, the description explains the return fields (system_prompt, user_prompt, response_schema, images, etc.) and the workflow (analyze in parallel, then call ingest). It is nearly complete, though could elaborate on 'analyze against the spec'.

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 0%, so the description compensates by explaining each parameter: project_id (required), slide_index (1-based position), iteration (default 0). This adds meaning beyond the bare schema. Could include valid ranges or constraints but suffices.

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 prepares the vision analysis task for one slide, explicitly notes 'No LLM call', and instructs the agent to read the screenshot, analyze, and call ingest_visual_qa_analysis. This differentiates it from sibling prepare_* tools by specifying its scope and next step.

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 tells when to use this tool (after capturing slides, before ingesting analysis) and advises to analyze slides in parallel. It explicitly mentions calling ingest_visual_qa_analysis next. It does not provide explicit when-not-to-use scenarios, but context is clear enough among siblings.

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