MCP server that bridges vision models to text-only coding models using Florence-2
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# videre-mcp [](https://github.com/Veedubin/Videre-MCP/actions/workflows/ci.yml) [](https://www.python.org/downloads/) [](LICENSE) [](https://docs.astral.sh/ruff/) MCP server that bridges vision models to text-only coding models using Florence-2. Non-vision LLMs can't see images — videre-mcp fixes that. It loads a Florence-2 vision model locally and exposes six MCP tools that convert images (including SVGs) and screenshots into structured text descriptions that any text-based model can consume. ``` Screenshot tool → videre-mcp (Florence-2) → Text description → Coding model ``` ## Installation ```bash pip install videre-mcp ``` Or with uv: ```bash uv pip install videre-mcp ``` Requires Python 3.11+ and ~300MB disk space for the Florence-2-base model weights (downloaded automatically on first use). ## Usage Add to your OpenCode configuration: ```json { "mcpServers": { "videre-mcp": { "command": "videre-mcp" } } } ``` Or run directly: ```bash videre-mcp # or python -m videre_mcp ``` ## Model Modes All Florence-2 tools support a `model_mode` parameter to balance speed and quality: - `"fast"` (default) — Uses **Florence-2-base**. Fast, lightweight, runs on CPU/GPU. - `"deep"` — Uses **MiniCPM-V 4.6**. Significantly higher quality for complex visual reasoning. - **Requires:** `pip install videre-mcp[deep]` - **Hardware:** ~8GB VRAM recommended. ## Tools ### `describe_image` Generate a natural language description of an image. **Parameters:** - `image_path` (str) — Path to the image file (supports PNG, JPEG, SVG) - `detail_level` (str, optional) — `"normal"` (default) for brief caption, `"high"` for detailed descripti…
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