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paddleocr-mcp

paddleocr-mcp

by Nicvank
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

parse_document

Extract text and analyze document structure from images or PDFs using a local OCR pipeline with automatic model selection.

Instructions

Run the latest local PaddleOCR-VL pipeline on an image or PDF.

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": "parse_document"
  }
}

Input Schema

NameRequiredDescriptionDefault
image_pathYes

Output Schema

NameRequiredDescriptionDefault
engineYes
markdownYes
source_nameYes
elapsed_secondsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

C2.9/5.0
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 behavioral disclosure burden. It only says a pipeline is 'run' and gives no indication of side effects, local model requirements, or failure behavior; 'local' is the only behavioral trait disclosed.

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 sentence with no filler. 'Latest' and 'local' are somewhat vague, but the core message is front-loaded and easy to parse.

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?

With one parameter and an output schema present, the description could be minimal, but it still omits path semantics and any guidance relative to sibling OCR tools. The 'latest' qualifier is not actionable, and the description does not fully equip an agent to invoke the tool correctly in ambiguous cases.

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?

The single parameter image_path has 0% schema description coverage, and the description partially compensates by saying the pipeline operates on an image or PDF. However, it does not explicitly state that image_path accepts a PDF path, what path formats are supported, or whether the file must be local.

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 identifies a specific action ('Run the latest local PaddleOCR-VL pipeline') and target inputs ('image or PDF'), making the core function clear. It does not explicitly differentiate itself from sibling tools ocr_image and smart_ocr, which weakens clarity in this tool family.

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 given on when to choose parse_document over ocr_image or smart_ocr, and no exclusions are stated. The phrase 'latest local' hints at a version or environment preference but does not provide an actionable decision criterion.

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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