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Document & FinTech Parser MCP

Document & FinTech Parser MCP

by Whambammy
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TypeScript
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ocr_bounding_box_dewarp_repair

Corrects camera skew, tilt, and binding curvature in OCR bounding boxes from photographed or scanned paper documents, restoring accurate text coordinates.

Instructions

Performs geometric perspective dewarping for photographed and scanned paper documents, correcting camera skew, tilt, and binding curvature. (0.035 USDC on Base L2)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "payload": "<payload>"
    },
    "name": "ocr_bounding_box_dewarp_repair"
  }
}

Input Schema

NameRequiredDescriptionDefault
payloadYesInput parameters or JSON string payload for the tool execution
paymentSignatureNoBase L2 USDC micropayment signature or transaction hash for x402 settlement

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full disclosure burden. It mentions a cost (0.035 USDC) but does not explain what happens to the input, what output is returned, whether any data is persisted, or any side effects. The transformation behavior is implied by purpose but not explicitly disclosed as a behavioral trait.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single efficient sentence that front-loads the core purpose and adds the cost. It is not verbose, and every word adds value. Minor deduction because the cost might have been better placed elsewhere, but overall structure is good.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex image-processing tool with no output schema and no annotations, the description is incomplete. It does not specify required input format, output representation, size limits, or any preconditions. The agent would have to guess how to construct the payload and what to expect in return, making it inadequate for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% (both parameters have descriptions), so the baseline is 3. The description adds no parameter-specific guidance; it does not clarify what the 'payload' should contain (e.g., image data format) or how paymentSignature relates to the operation. Thus it stays at baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Performs geometric perspective dewarping'), the target resource ('photographed and scanned paper documents'), and the specific corrections ('camera skew, tilt, and binding curvature'). This is specific enough to distinguish it from sibling tools, none of which mention dewarping.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description defines a clear usage context: it is for photographed or scanned paper documents that need geometric correction. This implicitly tells an agent when to use it, though it does not name alternatives or state when not to use it, which would push it to 5.

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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