AWS Rekognition MCP Server

What is AWS Rekognition MCP Server?

The AWS Rekognition MCP Server is a specialized MCP server that exposes the image and video analysis features of Amazon Rekognition as callable tools for Large Language Models (LLMs) and AI agents. It lets applications programmatically analyze images for object detection, scene recognition, facial analysis, and more using Amazon Rekognition, all within the secure and extendable workflow of the Model Context Protocol.

How to Configure AWS Rekognition MCP Server

• Install uv from Astral and ensure you have Python 3.10+.
• Configure your AWS credentials (using an AWS profile or environment variables) with permissions to access Amazon Rekognition.
• Add a configuration entry in your MCP client settings (such as mcp.json, cline_mcp_settings.json, or your tool’s designated config file) under mcpServers, for example:

  {
    "awslabs.amazon-rekognition-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.amazon-rekognition-mcp-server@latest"],
      "env": {
        "AWS_PROFILE": "your-aws-profile",
        "AWS_REGION": "us-east-1",
        "FASTMCP_LOG_LEVEL": "ERROR"
      }
    }
  }
  

• Optionally, set AWS_REGION and fine-tune logging or other environmental settings as needed.
• Save and restart your MCP client or LLM assistant to load the new server.

How to Use AWS Rekognition MCP Server

1. In your chat interface or AI coding tool, ensure the AWS Rekognition MCP Server is enabled and running.
2. When prompted to analyze an image, instruct the assistant to use Rekognition capabilities, e.g., “Analyze this image for objects using Rekognition.”
3. The MCP client will discover and call the appropriate tools (such as detect_labels, detect_faces) exposed by the Rekognition MCP Server.
4. Review and, if required, approve tool actions within your client (approval flows depend on your specific interface).
5. The server processes the image, calls Amazon Rekognition's API, and returns results (such as detected labels, faces, or text) directly into your AI-assisted workflow for use in further tasks or display.

Key Features

• Real-time integration with Amazon Rekognition’s image and video analysis APIs
• Supports multiple recognition tasks: label detection, face analysis, text-in-image detection, and scene classification
• Standardized tool invocation and result handling via MCP, compatible with a variety of AI toolchains and IDEs
• Secure, configurable execution using your own AWS credentials and infrastructure
• Easily composable with other AWS MCP Servers and developer tools within the same workflow

Use Cases

• Automated content moderation for user-uploaded images in chatbots, apps, or enterprise tools
• Building intelligent assistants that summarize, classify, or tag image collections for documentation, search, or cataloguing
• Extracting textual information from screenshots, forms, or signage images—integrated into larger document workflow automations
• Security, authentication, or compliance tasks automated by facial analysis or object detection in images
• Assisting code generation tasks that dynamically select ML models or logic based on analyzed image content
• Rapid prototyping of visual AI-driven features using natural language prompts in coding assistants or IDEs

FAQ

Q1: What permissions are required to run AWS Rekognition MCP Server?
You need AWS credentials with at least rekognition:DetectLabels, rekognition:DetectFaces, and other desired Rekognition API permissions for the resources you intend to analyze.

Q2: Can I use Rekognition MCP Server with local image files?
Yes; most clients support uploading local images for analysis, which the server processes either by uploading to S3 or via direct API (depending on configuration and tool).

Q3: How do I limit or approve requests for sensitive image data?
MCP workflow includes approval steps per tool invocation in many IDEs and chat clients; you can disable “autoApprove” and require manual review before each analysis.

Q4: Is video analysis supported?
The server focuses on core Rekognition APIs, including label and face detection for images; support for video analysis may depend on implementation roadmap or server updates.

Q5: Are the results from Rekognition sent to the LLM?
Yes. The analysis results (labels, detected faces, text, etc.) are returned through the MCP client to the AI assistant or agent, for use in context-aware answering or automating further actions.