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PaddleOCR MCP Server

PaddleOCR FastMCP

This project provides a lightweight Model Context Protocol (MCP) server designed to integrate PaddleOCR capabilities into various LLM applications.

Key Features

  • Currently Supported Tools
    • OCR: Performs text detection and recognition on images and PDF files.
    • PP-StructureV3: Identifies and extracts text blocks, titles, paragraphs, images, tables, and other layout elements from images or PDF files, converting the input into Markdown documents.
  • Supported Working Modes
    • Local Python Library: Runs PaddleOCR pipelines directly on the local machine. This mode requires a suitable local environment and hardware, and is ideal for offline use or privacy-sensitive scenarios.
    • AI Studio Community Service: Invokes services hosted on the PaddlePaddle AI Studio Community. This is suitable for quick testing, prototyping, or no-code scenarios.
    • Self-hosted Service: Invokes the user's self-hosted PaddleOCR services. This mode offers the advantages of serving and high flexibility. It is suitable for scenarios requiring customized service configurations, as well as those with strict data privacy requirements. Currently, only the basic serving solution is supported.

Examples:

The following showcases creative use cases built with PaddleOCR MCP server combined with other tools:

Demo 1

In Claude for Desktop, extract handwritten content from images and save to note-taking software Notion. The PaddleOCR MCP server extracts text, formulas and other information from images while preserving document structure.

note_to_notion note
  • Note: In addition to the PaddleOCR MCP server, this demo also uses the Notion MCP server.

Demo 2

In VSCode, convert handwritten ideas or pseudocode into runnable Python scripts that comply with project coding standards with one click, and upload them to GitHub repositories. The PaddleOCR MCP server extracts explicitly handwritten code from images for subsequent processing.

code_to_github

Demo 3

In Claude for Desktop, convert PDF documents or images containing complex tables, formulas, handwritten text and other content into locally editable files.

Demo 3.1

Convert complex PDF documents with tables and watermarks to editable doc/Word format:

pdf_to_file

Demo 3.2

Convert images containing formulas and tables to editable csv/Excel format:

table_to_excel1 table_to_excel2 table_to_excel3

Table of Contents

1. Installation

This section explains how to install the paddleocr-mcp library via pip.

  • For the local Python library mode, you need to install both paddleocr-mcp and the PaddlePaddle framework along with PaddleOCR, as per the PaddleOCR installation documentation.
  • For the AI Studio community service or the self-hosted service modes, if used within MCP hosts like Claude for Desktop, the server can also be run without installation via tools like uvx. See 2. Using with Claude for Desktop for details.

To install paddleocr-mcp using pip:

# Install the wheel
pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/mcp/paddleocr_mcp/releases/v0.2.0/paddleocr_mcp-0.2.0-py3-none-any.whl

# Or install from source
# git clone https://github.com/PaddlePaddle/PaddleOCR.git
# pip install -e mcp_server

To verify successful installation:

paddleocr_mcp --help

If the help message is printed, the installation succeeded.

2. Using with Claude for Desktop

This section explains how to use the PaddleOCR MCP server within Claude for Desktop. The steps are also applicable to other MCP hosts with minor adjustments.

2.1 Quick Start

  1. Install paddleocr-mcp

    Refer to 1. Installation. To avoid dependency conflicts, it is strongly recommended to install in an isolated virtual environment.

  2. Install PaddleOCR

    Install the PaddlePaddle framework and PaddleOCR, as per the PaddleOCR installation documentation.

  3. Add MCP Server Configuration

    Locate the claude_desktop_config.json configuration file:

    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%\Claude\claude_desktop_config.json
    • Linux: ~/.config/Claude/claude_desktop_config.json

    Edit the file as follows:

    {
  "mcpServers": {
    "paddleocr-ocr": {
      "command": "paddleocr_mcp",
      "args": [],
      "env": {
        "PADDLEOCR_MCP_PIPELINE": "OCR",
        "PADDLEOCR_MCP_PPOCR_SOURCE": "local"
      }
    }
  }
}

    Notes:

    • PADDLEOCR_MCP_PIPELINE_CONFIG is optional; if not set, the default pipeline configuration will be used. If you need to adjust the configuration, such as changing the model, please refer to the PaddleOCR documentation to export the pipeline configuration file, and set PADDLEOCR_MCP_PIPELINE_CONFIG to the absolute path of this configuration file.

    • Inference Performance Tips:

      If you encounter issues such as long inference time or insufficient memory during use, you may consider adjusting the pipeline configuration according to the following recommendations.

      • OCR Pipeline: It is recommended to switch to the mobile series models. For example, you can modify the pipeline configuration file to use PP-OCRv5_mobile_det for detection and PP-OCRv5_mobile_rec for recognition.

      • PP-StructureV3 Pipeline:

        • Disable unused features, e.g., set use_formula_recognition to False to disable formula recognition.
        • Use lightweight models, such as replacing the OCR model with the mobile version or switching to a lightweight formula recognition model like PP-FormulaNet-S.

        The following sample code can be used to obtain the pipeline configuration file, in which most optional features of the PP-StructureV3 pipeline are disabled, and some key models are replaced with lightweight versions.

        from paddleocr import PPStructureV3

pipeline = PPStructureV3(
    use_doc_orientation_classify=False, # Disable document image orientation classification
    use_doc_unwarping=False,            # Disable text image unwarping
    use_textline_orientation=False,     # Disable text line orientation classification
    use_formula_recognition=False,      # Disable formula recognition
    use_seal_recognition=False,         # Disable seal text recognition
    use_table_recognition=False,        # Disable table recognition
    use_chart_recognition=False,        # Disable chart parsing
    # Use lightweight models
    text_detection_model_name="PP-OCRv5_mobile_det",
    text_recognition_model_name="PP-OCRv5_mobile_rec",
    layout_detection_model_name="PP-DocLayout-S",
)

# The configuration file is saved to `PP-StructureV3.yaml`
pipeline.export_paddlex_config_to_yaml("PP-StructureV3.yaml")

    Important:

    • If paddleocr_mcp is not in your system's PATH, set command to the absolute path of the executable.

  4. Restart the MCP Host

    Restart Claude for Desktop. The paddleocr-ocr tool should now be available in the application.

2.2 MCP Host Configuration Details

In the configuration file for Claude for Desktop, you need to define how the MCP server is started. The key fields are as follows:

  • command: paddleocr_mcp (if the executable can be found in the PATH) or the absolute path.
  • args: Configurable command-line arguments, such as ["--verbose"]. See 4. Parameter Reference for details.
  • env: Configurable environment variables. See 4. Parameter Reference for details.

2.3 Working Modes Explained

You can configure the MCP server according to your requirements to run in different working modes. The operational procedures vary for different modes, which will be explained in detail below.

Mode 1: Local Python Library

See 2.1 Quick Start.

Mode 2: AI Studio Community Service

  1. Install paddleocr-mcp.
  2. Set up AI Studio community service.
    • Visit PaddlePaddle AI Studio Community and log in.
    • Under "PaddleX Pipeline" in the "More" section on the left, click in sequence: [Create Pipeline] - [OCR] - [General OCR] - [Deploy Directly] - [Start Deployment].
    • After deployment, obtain your service base URL (e.g., https://xxxxxx.aistudio-hub.baidu.com).
    • Get your access token from this page.
  3. Refer to the configuration example below to modify the contents of the claude_desktop_config.json file.
  4. Restart the MCP host.

Configuration example:

{
  "mcpServers": {
    "paddleocr-ocr": {
      "command": "paddleocr_mcp",
      "args": [],
      "env": {
        "PADDLEOCR_MCP_PIPELINE": "OCR",
        "PADDLEOCR_MCP_PPOCR_SOURCE": "aistudio",
        "PADDLEOCR_MCP_SERVER_URL": "<your-server-url>", 
        "PADDLEOCR_MCP_AISTUDIO_ACCESS_TOKEN": "<your-access-token>"
      }
    }
  }
}

Notes:

  • Replace <your-server-url> with your AI Studio service base URL, e.g., https://xxxxx.aistudio-hub.baidu.com. Make sure not to include the endpoint path (such as /ocr).
  • Replace <your-access-token> with your access token.

Important:

  • Do not expose your access token.

You may also train and deploy custom models on the platform.

Mode 3: Self-hosted Service

  1. In the environment where you need to run the PaddleOCR inference server, run the inference server as per the PaddleOCR serving documentation.
  2. Install paddleocr-mcp where the MCP server will run.
  3. Refer to the configuration example below to modify the contents of the claude_desktop_config.json file.
  4. Set PADDLEOCR_MCP_SERVER_URL (e.g., "http://127.0.0.1:8000").
  5. Restart the MCP host.

Configuration example:

{
  "mcpServers": {
    "paddleocr-ocr": {
      "command": "paddleocr_mcp",
      "args": [],
      "env": {
        "PADDLEOCR_MCP_PIPELINE": "OCR",
        "PADDLEOCR_MCP_PPOCR_SOURCE": "self_hosted",
        "PADDLEOCR_MCP_SERVER_URL": "<your-server-url>"
      }
    }
  }
}

Note:

  • Replace <your-server-url> with your service’s base URL (e.g., http://127.0.0.1:8000).

2.4 Using uvx

Currently, for both the AI Studio and self-hosted modes, starting the MCP server via uvx is also supported. With this approach, manual installation of paddleocr-mcp is not required. The main steps are as follows:

  1. Install uv.

  2. Modify claude_desktop_config.json. Example for self-hosted mode:

    {
  "mcpServers": {
    "paddleocr-ocr": {
      "command": "uvx",
      "args": [
        "--from",
        "paddleocr-mcp@https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/mcp/paddleocr_mcp/releases/v0.2.0/paddleocr_mcp-0.2.0-py3-none-any.whl",
        "paddleocr_mcp"
      ],
      "env": {
        "PADDLEOCR_MCP_PIPELINE": "OCR",
        "PADDLEOCR_MCP_PPOCR_SOURCE": "self_hosted",
        "PADDLEOCR_MCP_SERVER_URL": "<your-server-url>"
      }
    }
  }
}

    Due to the different startup methods used, the settings for command and args in the configuration file differ significantly from those described in 2.1 Quick Start. However, the command-line arguments and environment variables (such as PADDLEOCR_MCP_SERVER_URL) supported by the MCP service itself can still be set in the same way.

3. Running the Server

In addition to MCP hosts like Claude for Desktop, you can also run the PaddleOCR MCP server via the CLI.

To view help:

paddleocr_mcp --help

Example commands:

# OCR + AI Studio community service + stdio
PADDLEOCR_MCP_AISTUDIO_ACCESS_TOKEN=xxxxxx paddleocr_mcp --pipeline OCR --ppocr_source aistudio --server_url https://xxxxxx.aistudio-hub.baidu.com

# PP-StructureV3 + local Python library + stdio
paddleocr_mcp --pipeline PP-StructureV3 --ppocr_source local

# OCR + self-hosted service + Streamable HTTP
paddleocr_mcp --pipeline OCR --ppocr_source self_hosted --server_url http://127.0.0.1:8080 --http

You can find all the supported parameters of the PaddleOCR MCP server in 4. Parameter Reference.

4. Parameter Reference

You can control the MCP server via environment variables or CLI arguments.

Environment Variable CLI Argument Type Description Options Default
PADDLEOCR_MCP_PIPELINE --pipeline str Pipeline to run. "OCR", "PP-StructureV3" "OCR"
PADDLEOCR_MCP_PPOCR_SOURCE --ppocr_source str Source of PaddleOCR capabilities. "local" (local Python library), "aistudio" (AI Studio community service), "self_hosted" (self-hosted service) "local"
PADDLEOCR_MCP_SERVER_URL --server_url str Base URL for the underlying service (aistudio or self_hosted mode only). - None
PADDLEOCR_MCP_AISTUDIO_ACCESS_TOKEN --aistudio_access_token str AI Studio access token (aistudio mode only). - None
PADDLEOCR_MCP_TIMEOUT --timeout int Read timeout for the underlying requests (seconds). - 60
PADDLEOCR_MCP_DEVICE --device str Device for inference (local mode only). - None
PADDLEOCR_MCP_PIPELINE_CONFIG --pipeline_config str Path to pipeline config file (local mode only). - None
- --http bool Use Streamable HTTP instead of stdio (for remote/multi-client use). - False
- --host str Host for the Stremable HTTP mode. - "127.0.0.1"
- --port int Port for the Streamable HTTP mode. - 8000
- --verbose bool Enable verbose logging for debugging. - False

5. Known Limitations

  • In the local Python library mode, the current tools cannot process PDF document inputs that are Base64 encoded.
  • In the local Python library mode, the current tools do not infer the file type based on the model's file_type prompt, and may fail to process some complex URLs.
  • For the PP-StructureV3 pipeline, if the input file contains images, the returned results may significantly increase token usage. If image content is not needed, you can explicitly exclude it through prompts to reduce resource consumption.