OCR one image to text with dual-engine consensus, calibrated confidence and per-segment corroboration. Japanese-strong. Flat $0.05/image.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | japanese | |
| image_url | No | ||
| x_payment | No | Optional: base64 X-PAYMENT header value (x402 exact scheme, USDC on Base mainnet). Omit it to receive the payment requirements (accepts[] + docs) for this call without being charged. | |
| image_base64 | No |
Changes observed during successful MCP inspections.
Input schema / properties / x_paymentAdded value: +{
+ "description": "Optional: base64 X-PAYMENT header value (x402 exact scheme, USDC on Base mainnet). Omit it to receive the payment requirements (accepts[] + docs) for this call without being charged.",
+ "type": "string"
+}Input schema / requiredAdded value: +[]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses internal processing behavior (dual-engine consensus, confidence, corroboration) and adds pricing transparency ('Flat $0.05/image'). It doesn't cover error handling or auth, but for a simple OCR tool this is sufficient.
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?
One sentence, front-loaded with the core action, then concise additional details. No wasted words or 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?
No output schema exists, so the description should describe returns; it mentions 'to text' and confidence/corroboration, implying structure. However, it omits the dual image input methods and the payment flow (even though x_payment is in the schema), leaving contextual gaps for complete usage.
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 25% (only x_payment explained). The description adds little about parameters: 'Japanese-strong' hints at lang, but provides no explanation of image_url vs image_base64, leaving critical input ambiguity. It fails to compensate for the low schema coverage.
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?
Specific verb 'OCR' and resource 'image' clearly state the tool's function. The mention of 'dual-engine consensus, calibrated confidence and per-segment corroboration' adds distinctiveness and differentiates it from sibling tools like parse_document or structured_extract.
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?
'Japanese-strong' implies primary use for Japanese text, but no explicit when-to-use vs alternatives. The cost point suggests budget considerations, but there's no clear guidance on choosing this over other OCR or parsing tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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