ingest_visual_qa_fix

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

With no annotations, the description carries full burden. It discloses validation, saving, re-rendering, restoration of images/slide_type, and re-running capture/analysis. It does not mention authorization or failure handling beyond status, but covers core behavior well.

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 efficiently structured: a one-line summary, followed by usage guidance, then Args, then Returns. 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 3 required parameters, no enums, and presence of output schema, the description covers purpose, usage, parameters, and return values comprehensively. It is complete for an AI agent to understand and invoke 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?

Despite 0% schema description coverage, the description's Args section adds meaning: project_id is required, slide_index is 1-based, fix_json is the corrected slide spec JSON. This goes beyond the bare 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?

The description clearly states the tool ingests a client-generated fix, with actions validate, save, re-render HTML. It distinguishes itself from the sibling 'prepare_visual_qa_fix' by specifying it should be called after that 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?

Explicitly says 'Call AFTER prepare_visual_qa_fix', providing clear sequencing. Also mentions it restores LLM-unproducible content and re-runs verification up to max_iterations, giving context for when to use.

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