multimodal_task

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

With no annotations, the description must carry the full burden of behavioral disclosure. It only describes the action of sending a conversation to multimodal models, with no information about API key requirements, rate limits, response format, error handling, side effects, or the behavior of the model call. This is minimal behavioral 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 exceptionally concise: two short sentences that immediately convey the tool's purpose without any filler. It is front-loaded with the core function, and every word earns its place, making it a model of efficiency.

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?

For a tool with 6 parameters, no output schema, and no annotations, this two-sentence description is insufficiently complete. It fails to explain the meaning of the model, temperature, max_tokens, stream, and top_p parameters, and it provides no information about return values or potential errors. An agent would struggle to invoke this tool correctly without additional context.

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 description coverage is only 33% (model and messages have descriptions; top_p, stream, max_tokens, temperature do not). The description adds no meaningful detail for the undocumented parameters—it only vaguely references the messages content. This does not compensate for the low schema coverage, leaving users without a clear understanding of the other parameters.

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's function: perform multimodal tasks by sending a conversation with mixed text and image content to multimodal models. It uses specific verbs ('perform'/'send') and a concrete resource, and it differentiates from text-only or image-only sibling tools by emphasizing the combination of modalities, though it doesn't explicitly name alternatives.

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 implies this tool is for multimodal reasoning with both text and images, but it doesn't provide explicit guidance on when to use it versus alternatives like chat_completion or analyze_image. There is no mention of when-not-to-use or alternative tools, so the usage context is only implied, not clearly stated.

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