Frequently Asked Questions (FAQ) - VisionAgent MCP Server

Q: What is the VisionAgent MCP Server?

A: The VisionAgent MCP Server is a lightweight server that translates tool calls from MCP-compatible clients into authenticated HTTPS requests to Landing AI’s VisionAgent REST APIs. It enables natural-language computer-vision commands from your editor without custom code.

Q: What is MCP (Model Context Protocol)?

A: MCP is an open protocol that standardizes how applications provide context to LLMs, allowing AI models to access external data sources and tools.

Q: What are the key use cases supported by VisionAgent MCP Server?

A: The server supports use cases like agentic-document-analysis for parsing PDFs, text-to-object-detection for identifying objects, text-to-instance-segmentation for pixel-perfect masks, activity-recognition for videos, and depth-pro for depth estimation.

Q: What are the prerequisites for using the VisionAgent MCP Server?

A: You need Node.js (version 20 LTS or higher), a VisionAgent account with an API key, and an MCP client like Claude Desktop, Cursor, or Cline.

Q: How do I configure my MCP client to work with the VisionAgent MCP Server?

A: You need to configure your MCP client with the command to run the vision-tools-mcp, set the VISION_AGENT_API_KEY, OUTPUT_DIRECTORY, and optionally IMAGE_DISPLAY_ENABLED in the environment variables.

Q: What do I do if I get an authentication error?

A: Verify your VISION_AGENT_API_KEY is correct and active, ensure your free tier isn’t rate-limited, and check if outbound HTTPS to api.va.landing.ai is blocked by a proxy/VPN.

Q: What does the IMAGE_DISPLAY_ENABLED configuration option do?

A: If set to true, the server will render masks, boxes, or depth maps to files. If false, rendering is skipped.

Q: How can UBOS platform help me with VisionAgent MCP Server?

A: UBOS platform provides integration capabilities such as AI Agent orchestration. Our platform help you orchestrate AI Agents, connect them with your enterprise data, build custom AI Agents with your LLM model and Multi-Agent Systems.