Florence‑2 MCP Server

Overview

The Florence‑2 MCP server bridges the gap between AI assistants and visual data by exposing Microsoft’s Florence‑2 model as a ready‑to‑use service. It solves the common problem of extracting meaningful text or descriptive information from images and PDF files that are stored locally or reachable via HTTP. By offering OCR and captioning capabilities through a simple Model Context Protocol interface, developers can enrich conversational agents with visual understanding without having to host or fine‑tune large vision models themselves.

At its core, the server implements two lightweight tools: ocr and caption. The ocr tool takes an image file path or URL, runs Florence‑2’s optical character recognition pipeline, and returns the detected text. The caption tool processes an image in a similar fashion but produces natural‑language captions that summarize the visual content. These outputs can be fed back into an assistant’s prompt or used to trigger downstream logic, enabling use cases such as automated document digitization, image‑based search indexing, or multimodal question answering.

For developers integrating AI workflows, the server is a drop‑in component. It can be invoked from Claude Desktop, Goose CLI/desktop, or LM Studio simply by adding the MCP configuration. The tools accept minimal arguments—just a source path or URL—making it trivial to script bulk processing or embed the calls in larger pipelines. Because Florence‑2 is a single large model, the server handles all heavy lifting, leaving developers free to focus on business logic rather than infrastructure.

Unique advantages include Florence‑2’s robust performance across diverse document types and its ability to generate high‑quality captions in a single pass. The server’s design follows MCP best practices, providing clear tool definitions and argument schemas that integrate seamlessly with existing extension ecosystems. This consistency allows AI assistants to discover and call the OCR or captioning functions automatically, supporting dynamic workflow construction.

In real‑world scenarios, teams can use the server to convert scanned invoices into searchable text, generate alt‑text for accessibility compliance, or create descriptive metadata for image repositories. By exposing Florence‑2 as an MCP service, the project empowers developers to unlock visual intelligence in a scalable, maintainable way without reinventing the wheel.