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Florence-2 MCP Server: Advanced Image Processing & AI Scalability - MCP Implementation

Florence-2 MCP Server: Advanced Image Processing & AI Scalability

Florence-2 MCP Server: Unleash next-level image processing with enterprise-grade speed and precision. Effortless scalability for your AI-driven workflows.

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This tool saved users approximately 14030 hours last month!

About Florence-2 MCP Server

What is Florence-2 MCP Server: Advanced Image Processing & AI Scalability?

At its core, the Florence-2 MCP Server is a robust solution designed to handle image and PDF processing tasks with precision. Leveraging the capabilities of the Florence-2 model from Microsoft, this server enables users to extract text via OCR or generate descriptive captions for images stored locally or on remote servers. Its architecture emphasizes scalability, making it a reliable choice for applications requiring efficient image analysis.

How to Use Florence-2 MCP Server: Advanced Image Processing & AI Scalability?

For Claude Desktop Users

Configure the server by editing the claude_desktop_config.json file. Insert the provided JSON snippet under mcpServers, ensuring the uvx command points to the GitHub repository. A restart of the application is mandatory after these changes.

Goose CLI & Goose Desktop Setup

In Goose CLI, update the config.yaml with the specified Bear extension parameters. For Goose Desktop, add a new Standard IO extension with the exact command string provided. Users must cross-verify the configuration syntax to avoid runtime errors.

Florence-2 MCP Server Features

Key Features of Florence-2 MCP Server: Advanced Image Processing & AI Scalability?

  • Multi-source flexibility: Processes files from both local storage and cloud-based endpoints seamlessly.
  • Context-aware OCR: Delivers high-accuracy text extraction even from low-resolution scans or complex layouts.
  • Descriptive captions: Generates human-like summaries for images, ideal for metadata tagging or accessibility needs.
  • Modular scalability: Designed to handle varying workloads, making it suitable for both small-scale projects and enterprise-level operations.

Use Cases of Florence-2 MCP Server: Advanced Image Processing & AI Scalability?

Common applications include:

  • Automating document digitization for legal or administrative workflows.
  • Creating accessible content libraries by adding searchable text layers to images.
  • Powering e-commerce platforms with product image descriptions for SEO optimization.
  • Accelerating research workflows by extracting data from scientific diagrams or historical archives.

Florence-2 MCP Server FAQ

FAQ: Florence-2 MCP Server: Advanced Image Processing & AI Scalability?

Q: Does this require specialized hardware?

A: While GPU acceleration improves performance, the server runs efficiently on standard CPU setups for moderate workloads.

Q: How are errors handled during batch processing?

A: The system logs detailed error reports and skips problematic files automatically, ensuring uninterrupted processing of valid inputs.

Q: Can I customize the output format?

A: Yes, the API supports JSON, CSV, and plain text outputs, allowing seamless integration with existing workflows.

Content

Florence-2 MCP Server

Python Application GitHub License pre-commit Ruff smithery badge

An MCP server for processing images using Florence-2.

You can process images or PDF files stored on a local or web server to extract text using OCR (Optical Character Recognition) or generate descriptive captions summarizing the content of the images.

Installation

For Claude Desktop

To configure this server for Claude Desktop, edit the claude_desktop_config.json file with the following entry under mcpServers:

{
  "mcpServers": {
    "florence-2": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/jkawamoto/mcp-florence2",
        "mcp-florence2"
      ]
    }
  }
}

After editing, restart the application. For more information, see: For Claude Desktop Users - Model Context Protocol.

For Goose CLI

To enable the Bear extension in Goose CLI, edit the configuration file ~/.config/goose/config.yaml to include the following entry:

extensions:
  bear:
    name: Florence-2
    cmd: uvx
    args: [ --from, git+https://github.com/jkawamoto/mcp-florence2, mcp-florence2 ]
    enabled: true
    type: stdio

For Goose Desktop

Add a new extension with the following settings:

  • Type : Standard IO
  • ID : florence-2
  • Name : Florence-2
  • Description : An MCP server for processing images using Florence-2
  • Command : uvx --from git+https://github.com/jkawamoto/mcp-florence2 mcp-florence2

For more details on configuring MCP servers in Goose Desktop, refer to the documentation: Using Extensions - MCP Servers.

Tools

ocr

Process an image file or URL using OCR to extract text.

Arguments:

  • src : A file path or URL to the image file that needs to be processed.

caption

Processes an image file and generates captions for the image.

Arguments:

  • src : A file path or URL to the image file that needs to be processed.

License

This application is licensed under the MIT License. See the LICENSE file for more details.

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