analyze_image

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It discloses payment requirement, model used, and capabilities, but does not detail failure modes, rate limits, or whether the operation is read-only (assumed from analysis). The cost and model information are valuable behavioral traits.

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 relatively concise (four sentences) and front-loaded with the core purpose. However, the sentence 'Uses Qwen VL — multimodal vision-language model with strong OCR, chart reading, and spatial reasoning.' could be slightly more concise without losing meaning. Overall, it efficiently communicates key 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 the tool has 4 parameters (3 required) and no output schema, the description covers essential aspects: purpose, prerequisites (payment flow), model details, and parameter notes. It provides sufficient context for an AI agent to decide when and how to invoke the tool correctly, including the mandatory payment step.

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 100% (all parameters have descriptions). The description adds value beyond the schema by explaining the payment requirement for 'paymentId', noting that 'modelId' is optional, and mentioning the model's capabilities (Qwen VL) which helps interpret the 'prompt' parameter. This enhances understanding beyond the schema alone.

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 explicitly states the tool's purpose: 'Analyze and describe image content, answer visual questions, extract information from screenshots or photos.' It also specifies the underlying model (Qwen VL) and its capabilities (OCR, chart reading, spatial reasoning), making it distinct from sibling tools like 'detect_objects' or 'generate_image'.

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 clearly states the prerequisite: 'Requires create_payment with toolName='analyze_image'.' It also mentions cost (21 sats) and that no API key is needed. However, it does not explicitly differentiate when to use this tool versus other image-related tools (e.g., 'detect_objects', 'generate_image'), relying on the purpose to imply appropriate contexts.

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