landing-ai/vision-agent-mcp

Developer tools

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Repository Install

This tool has been deprecated. Use Agentic Document Extraction instead.

Обзор

This project is and subject to breaking changes until v1.0. Modern LLM “agents” call external tools through the is a lightweight, side-car MCP server that runs locally on STDIN/STDOUT, translating each tool call from an MCP-compatible client (Claude Desktop, Cursor, Cline, etc.) into an authenticated HTTPS request to Landing AI’s VisionAgent REST APIs. The response JSON, plus any images or masks, is streamed back to the model so that you can issue natural-language computer-vision and document-analysis commands from your editor without writing custom REST code or loading an extra SDK. https://github.com/user-attachments/assets/2017fa01-0e7f-411c-a417-9f79562627b7 Run whenever VisionAgent releases new endpoints. The script fetches the latest OpenAPI spec and regenerates the local tool map automatically. If you do not have a VisionAgent API key, create an account and obtain your API key. 3. Open your MCP-aware client. 4. Download street.

README

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VisionAgent MCP Server

Beta – v0.1
This project is early access and subject to breaking changes until v1.0.

VisionAgent MCP Server v0.1 - Overview

Modern LLM “agents” call external tools through the Model Context Protocol (MCP). VisionAgent MCP is a lightweight, side-car MCP server that runs locally on STDIN/STDOUT, translating each tool call from an MCP-compatible client (Claude Desktop, Cursor, Cline, etc.) into an authenticated HTTPS request to Landing AI’s VisionAgent REST APIs. The response JSON, plus any images or masks, is streamed back to the model so that you can issue natural-language computer-vision and document-analysis commands from your editor without writing custom REST code or loading an extra SDK.

📸 Demo

https://github.com/user-attachments/assets/2017fa01-0e7f-411c-a417-9f79562627b7

🧰 Supported Use Cases (v0.1)

Capability	Description
`agentic-document-analysis`	Parse PDFs / images to extract text, tables, charts, and diagrams taking into account layouts and other visual cues. Web Version here.
`text-to-object-detection`	Detect free-form prompts (“all traffic lights”) using OWLv2 / CountGD / Florence-2 / Agentic Object Detection (Web Version here); outputs bounding boxes.
`text-to-instance-segmentation`	Pixel-perfect masks via Florence-2 + Segment-Anything-v2 (SAM-2).
`activity-recognition`	Recognise multiple activities in video with start/end timestamps.
`depth-pro`	High-resolution monocular depth estimation for single images.

Run npm run generate-tools whenever VisionAgent releases new endpoints. The script fetches the latest OpenAPI spec and regenerates the local tool map automatically.

🚀 Quick Start

Get Your VisionAgent API Key

Открыть полный README на GitHub

Установка

npx vision-tools-mcp

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Конфигурация

# 1  Install
npm install -g vision-tools-mcp

# 2  Configure your MCP client with the following settings:
{
  "mcpServers": {
    "VisionAgent": {
      "command": "npx",
      "args": ["vision-tools-mcp"],
      "env": {
        "VISION_AGENT_API_KEY": "<YOUR_API_KEY>",
        "OUTPUT_DIRECTORY": "/path/to/output/directory",
        "IMAGE_DISPLAY_ENABLED": "true" # or false, see below
      }
    }
  }
}