by prooflie
Enable advanced deepfake detection in images using Proofly API. Get real/fake probability scores with cutting-edge deepf
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.
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.
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.
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.
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
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
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
Share your MCP server with the developer community
We wired Proofly (Deepfake Detection) into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Strong directory entry: Proofly (Deepfake Detection) surfaces stars and publisher context so we could sanity-check maintenance before adopting.
According to our notes, Proofly (Deepfake Detection) benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Proofly (Deepfake Detection) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
I recommend Proofly (Deepfake Detection) for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
We evaluated Proofly (Deepfake Detection) against two servers with overlapping tools; this profile had the clearer scope statement.
Proofly (Deepfake Detection) is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Useful MCP listing: Proofly (Deepfake Detection) is the kind of server we cite when onboarding engineers to host + tool permissions.
Proofly (Deepfake Detection) reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
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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Install and just write 'proofly it' URL to content or analyze it URL to content for deepfake face swap analysis.
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
}
}
Claude Desktop:
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"
]
}
}
}
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.Analyzes an image from a URL for deepfake detection.
Analyzes an image provided as a base64 string for deepfake detection.
Checks the status of a deepfake analysis session.
Gets detailed information about a specific face detected in an image analysis session.
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ 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.