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paddleocr-mcp

paddleocr-mcp

by Nicvank
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

ocr_image

Extract text from any image by providing its file path, using a local PaddleOCR pipeline for accurate recognition.

Instructions

Run the latest local PaddleOCR text-recognition pipeline on an image.

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": {
      "image_path": "<image_path>"
    },
    "name": "ocr_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
languageNoch
image_pathYes

Output Schema

NameRequiredDescriptionDefault
textYes
blocksNo
engineYes
source_nameYes
elapsed_secondsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

C2.6/5.0
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 burden of behavioral disclosure. It mentions that the pipeline is local and uses PaddleOCR, but does not disclose side effects, output behavior, performance considerations, or whether the image must be a local path.

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 front-loaded sentence with no wasted words. It is concise, but the terseness leaves out important operational details that an agent would need.

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?

The tool is simple with only two parameters and an output schema exists, but the description still lacks usage guidance and parameter semantics. An agent cannot confidently choose this tool over smart_ocr or know what language values are supported.

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

Parameters1/5

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

Schema description coverage is 0%, and the description does not add meaning to either parameter. It never mentions image_path as the required input or explain the language parameter's default or supported values. The description fails to compensate for the bare schema.

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

Purpose4/5

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

The description states a specific verb and resource: 'Run the latest local PaddleOCR text-recognition pipeline on an image.' This clearly communicates the core action and object. However, it does not distinguish this tool from siblings like smart_ocr or parse_document.

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

Usage Guidelines2/5

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

No guidance is provided about when to use ocr_image versus parse_document or smart_ocr. There are no exclusions, prerequisites, or contextual signals to help an agent choose this tool over alternatives.

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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