Proofly MCP Integration

Proofly MCP Integration

An MCP server that provides deepfake detection capabilities, allowing clients to analyze images for authenticity via Proofly's API.

Category
Visit Server

Tools

analyze-image

Analyzes an image provided as a base64 string for deepfake detection.

analyze

Analyzes an image from a URL for deepfake detection.

check-session-status

Check the status of a deepfake analysis session.

get-face-details

Get detailed information about a specific face detected in an image.

README

Proofly MCP Integration

This document describes two ways to integrate Proofly's deepfake detection capabilities with Model Context Protocol (MCP) compatible clients:

  1. Via a Hosted MCP Server (https://mcp.proofly.ai): For clients that connect to MCP servers using a URL (e.g., Cursor, Cascade/Windsurf).
  2. Via a Local CLI MCP Server (proofly-mcp npm package): For clients that can execute a local command for an MCP server (e.g., Claude Desktop).

Both integration methods ultimately use the Proofly API (https://api.proofly.ai) for analysis.


1. Using the Hosted MCP Server (https://mcp.proofly.ai)

This is the recommended method for MCP clients that connect to servers via HTTP/SSE URLs, such as Cursor, Cascade/Windsurf, etc.

Configuration Examples (for URL-based clients)

Add one of the following configurations to your MCP client (e.g., in mcp_config.json):

A. Streaming (SSE - Recommended where supported):

{
  "proofly": {
    "serverUrl": "https://mcp.proofly.ai/sse",
    "supportedMethods": [
      "analyze-image",
      "analyze",
      "get-face-details",
      "check-session-status"
    ],
    "auth": { "type": "none" } // Or your specific auth if Proofly API https:/get.proofly.ai requires it
  }
}

B. Standard HTTP (Non-streaming):

{
  "proofly": {
    "serverUrl": "https://mcp.proofly.ai/mcp",
    "supportedMethods": [
      "analyze-image",
      "analyze",
      "get-face-details",
      "check-session-status"
    ],
    "auth": { "type": "none" } // Or your specific auth if Proofly API https:/get.proofly.ai requires it
  }
}

Note: The mcp.proofly.ai server is a separate deployment. This proofly-mcp npm package is not used to run or configure mcp.proofly.ai.


2. Using the Local CLI MCP Server (proofly-mcp npm package)

This proofly-mcp npm package provides a command-line tool that acts as an MCP server. It's designed for MCP clients that can execute a local command and communicate with it via stdio (e.g., Claude Desktop).

Features of proofly-mcp CLI

  • Acts as a local MCP server communicating via stdio.
  • Analyzes images for deepfake detection (from Base64 or URL).
  • Checks session status for an analysis.
  • Gets detailed information about specific detected faces.

Installation of proofly-mcp CLI

Global Installation (Recommended for direct use by clients like Claude Desktop):

npm install -g proofly-mcp

Local Installation (For programmatic use or if preferred):

npm install proofly-mcp

Environment Variables for proofly-mcp CLI (Optional)

  • PROOFLY_API_KEY: Your Proofly API key. The proofly-mcp CLI will use this API key if the variable is set when communicating with Proofly API https://get.proofly.ai.

Configuration Examples (for command-based clients using proofly-mcp)

Claude Desktop:

Add to your Claude Desktop config file (e.g., claude_desktop_config.json). The recommended way is to use npx to ensure you are running the latest version without requiring a global install:

{
  "mcpServers": {
    "proofly": {
      "command": "npx",
      "args": [
        "-y", // The -y flag might be specific to your npm/npx version or aliasing for auto-confirmation.
              // Alternatively, for most npx versions: "proofly-mcp@latest"
        "proofly-mcp@latest"
      ],
      "supportedMethods": [
        "analyze-image",
        "analyze",
        "get-face-details",
        "check-session-status"
      ]
    }
  }
}

Alternatively, if you have proofly-mcp installed globally (npm install -g proofly-mcp), you can use:

{
  "mcpServers": {
    "proofly": {
      "command": "proofly-mcp",
      "args": [],
      "supportedMethods": [
        "analyze-image",
        "analyze",
        "get-face-details",
        "check-session-status"
      ]
    }
  }
}
  • Claude Desktop will execute the specified command, which then acts as the MCP server.

Other command-capable MCP Clients:

If your MCP client can launch a local command, configure it to run proofly-mcp. Conceptual example (actual config varies by client):

{
  "mcpServers": {
    "proofly": {
      "type": "command",
      "command": "proofly-mcp",
      "supportedMethods": [
        "analyze-image",
        "analyze",
        "get-face-details",
        "check-session-status"
      ]
    }
  }
}

Available MCP Methods

The following methods are supported by both the https://mcp.proofly.ai hosted server and the proofly-mcp CLI server.

analyze-image

Analyzes an image provided as a base64 string for deepfake detection.

Parameters:

  • imageBase64: string - Base64 encoded image data.
  • filename: string - Original filename with extension (e.g., 'image.jpg').
  • format: "text" | "json" (optional, default: "text") - Output format.

analyze

Analyzes an image from a URL for deepfake detection.

Parameters:

  • imageUrl: string - URL of the image to analyze.
  • format: "text" | "json" (optional, default: "text") - Output format.

check-session-status

Checks the status of a deepfake analysis session.

Parameters:

  • sessionUuid: string - Session UUID to check status for.
  • format: "text" | "json" (optional, default: "text") - Output format.

get-face-details

Gets detailed information about a specific face detected in an image analysis session.

Parameters:

  • sessionUuid: string - Session UUID from a previous analysis.
  • faceIndex: number - Index of the face to get details for (starting from 0).
  • format: "text" | "json" (optional, default: "text") - Output format.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured