agnes_vision

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

No annotations are provided, so the description bears the full transparency burden. It mentions sending images and text but does not disclose behaviors like rate limits, error handling for invalid images, authentication needs, or output format. The description covers basic operation but lacks depth on behavioral nuances.

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 at three sentences, front-loading the core purpose first. Every sentence serves a purpose: stating the capability, explaining input types, and listing models. No extraneous information.

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 7 parameters and no output schema, the description adequately covers the main input types and use case. It explains the image formats and instruction purpose. However, it omits description of the return value (expected to be model-generated text) and does not mention streaming behavior. Still, it is sufficiently complete for a typical vision tool.

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 71%, so the baseline is moderate. The description adds meaning beyond the schema by clarifying that 'images' can be public URLs or data URIs, and that 'instruction' is a text prompt. However, for parameters like temperature, max_tokens, and stream, it provides no additional context beyond the 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's purpose as multimodal understanding, specifying that it accepts images (URLs or data URIs) and a text instruction to describe, analyze, OCR, or answer questions. It also lists the available models. This distinguishes it from sibling tools like agnes_chat (text-only) and agnes_image (image generation).

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?

While the description explains what the tool does, it does not explicitly state when to use it versus alternatives or when not to use it. The context of sibling tools implies vision tasks, but no direct guidance is given for exclusion criteria (e.g., text-only queries should use agnes_chat).

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