auth-securityai-ml

Proofly (Deepfake Detection)

prooflie

by prooflie

Enable advanced deepfake detection in images using Proofly API. Get real/fake probability scores with cutting-edge deepf

Enables deepfake detection in images through Proofly API integration, providing detailed analysis results including real/fake probability scores and individual model results for each detected face.

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Remote — zero setup requiredMultiple deployment options (SSE streaming or HTTP)Per-face detailed analysis

best for

  • / Content moderation and verification
  • / Security teams validating user-submitted images
  • / Media organizations fact-checking photos
  • / Developers building authentication systems

capabilities

  • / Analyze images from URLs for deepfake detection
  • / Process base64-encoded images for face authenticity
  • / Get detailed face-by-face analysis results
  • / Check analysis session status and progress
  • / Generate real/fake probability scores per face

what it does

Detects deepfakes and face swaps in images by analyzing faces and providing probability scores for authenticity. Works with both image URLs and base64-encoded images.

about

Proofly (Deepfake Detection) is an official MCP server published by prooflie that provides AI assistants with tools and capabilities via the Model Context Protocol. Enable advanced deepfake detection in images using Proofly API. Get real/fake probability scores with cutting-edge deepf It is categorized under auth security, ai ml. This server exposes 4 tools that AI clients can invoke during conversations and coding sessions.

how to install

You can install Proofly (Deepfake Detection) in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Proofly (Deepfake Detection) is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Proofly MCP Integration

Install and just write 'proofly it' URL to content or analyze it URL to content for deepfake face swap analysis.

  1. For clients that connect to MCP servers using a URL (e.g., Cursor, Cascade/Windsurf)

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
  }
}
  1. For clients that can execute a local command for an MCP server (e.g., Claude Desktop)

Claude Desktop:

  1. Run: npx proofly-mcp@latest
  2. Add to your Claude Desktop config file (e.g., claude_desktop_config.json)
{
  "mcpServers": {
    "proofly": {
      "command": "npx",
      "args": [
        "-y", // The -y flag might be specific to your npm/npx version or aliasing for auto-confirmation.
        "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"
      ]
    }
  }
}

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"
      ]
    }
  }
}

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.

Available MCP Methods

analyze

Analyzes an image from a URL for deepfake detection.

analyze-image

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

check-session-status

Checks the status of a deepfake analysis session.

get-face-details

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

FAQ

What is the Proofly (Deepfake Detection) MCP server?
Proofly (Deepfake Detection) is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Proofly (Deepfake Detection)?
This profile displays 33 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.533 reviews
  • Pratham Ware· Dec 24, 2024

    We wired Proofly (Deepfake Detection) into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Layla Menon· Dec 24, 2024

    Strong directory entry: Proofly (Deepfake Detection) surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Yusuf Jackson· Dec 16, 2024

    According to our notes, Proofly (Deepfake Detection) benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Yash Thakker· Nov 15, 2024

    Proofly (Deepfake Detection) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Yuki Park· Nov 15, 2024

    I recommend Proofly (Deepfake Detection) for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Zara Dixit· Nov 11, 2024

    We evaluated Proofly (Deepfake Detection) against two servers with overlapping tools; this profile had the clearer scope statement.

  • Evelyn Gupta· Oct 26, 2024

    Proofly (Deepfake Detection) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Dhruvi Jain· Oct 6, 2024

    Useful MCP listing: Proofly (Deepfake Detection) is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Emma Jackson· Oct 6, 2024

    Proofly (Deepfake Detection) reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Yuki Farah· Oct 2, 2024

    Proofly (Deepfake Detection) has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

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