minimax_vision_understand

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

With no annotations, the description carries the burden of disclosing behavior. It discloses the default model, support for video understanding, 1M context, and adaptive thinking, and adds a note about the 'thinking' parameter being ignored on M2.x models. This is transparent about operational nuances, though it doesn't explicitly state the tool is read-only or describe the output format, which are minor gaps.

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 a tight three sentences: it states the core purpose, lists supported formats, and highlights model capabilities. Every sentence provides useful information without fluff, and it is front-loaded with the primary action.

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 has 5 parameters and no output schema, the description covers a lot: input formats, default model, model capabilities, and parameter nuance. It doesn't explicitly state the return type (likely text analysis), which would be helpful, but it is reasonably complete for an AI-driven 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?

The schema covers all 5 parameters, so the baseline is 3. The description adds value by explaining the image parameter supports both local files and URLs (reinforcing schema), and crucially notes that the 'thinking' parameter is ignored on M2.x models. This extra context goes beyond the schema's own descriptions.

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 'Analyze an image using MiniMax vision models' with a specific verb and resource, and distinguishes the tool's capabilities (local/remote image support, model defaults). This is a clear, specific purpose statement.

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 when to use the tool (for image analysis) and even clarifies supported input formats and URL vs local files. However, with no sibling tools to differentiate from, explicit alternatives or exclusions are absent. The guidance is adequate but not explicit about when not to use it.

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