MCP Content Credentials Server
Enables reading and verifying C2PA Content Credentials from images and videos through both embedded manifests and invisible watermarks. Supports direct filesystem access, URL checking, and provides structured provenance data including creator identity, AI generation info, and content authenticity.
README
MCP Content Credentials Server
MCP (Model Context Protocol) server for reading C2PA Content Credentials from images and videos. Detects credentials from both embedded manifests and invisible watermarks.
Features
- 🔍 Embedded C2PA Detection - Read manifests from file metadata
- 🌊 TrustMark Watermark Detection - Detect credentials in image pixels (survives social media!)
- 🌐 URL Support - Check credentials from web URLs
- 📂 Direct Filesystem Access - Claude can browse your directories
- ⚡ Smart Detection - Checks embedded first, watermark as fallback
- 🤖 Automatic Installation - Zero configuration setup
- 📋 Structured Output - Human-readable parsed data
- 🛡️ Production Ready - Full error handling and logging
- 🌐 REST API - HTTP endpoints for ChatGPT and web integration
Quick Start
# 1. Clone
git clone https://github.com/noga7/mcp-content-credentials.git
cd mcp-content-credentials
# 2. Install (automatic: installs c2patool + TrustMark)
npm install
# 3. Build
npm run build
# 4. Configure Claude Desktop
# Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"content-credentials": {
"command": "node",
"args": ["/absolute/path/to/mcp-content-credentials/build/index.js"]
}
}
}
# 5. Restart Claude Desktop
# Optional: Start REST API for ChatGPT/web access
npm run start:api
# Server runs on http://localhost:3000
REST API (for ChatGPT & Web Apps)
Want to use this with ChatGPT or your own web app? Start the HTTP REST API:
npm run start:api
The server runs on http://localhost:3000. See REST-API.md for full documentation.
For ChatGPT: Use ngrok to expose your local server, or deploy to Render/Railway. See REST-API.md for instructions.
# Quick test
curl "http://localhost:3000/verify-url?url=https://example.com/image.jpg"
Prerequisites
- Node.js v18+
- Python 3.8.5+ (for TrustMark watermarks)
All other dependencies auto-install during npm install:
- ✅ c2patool (Homebrew on macOS, binary on Linux)
- ✅ TrustMark Python package (via pip)
Manual Installation (if auto-install fails)
# c2patool
brew install contentauth/tools/c2patool # macOS
# TrustMark
pip3 install trustmark Pillow
# Or retry auto-install
npm run install-deps
Usage
Check a Specific File
"Check content credentials in ~/Desktop/photo.jpg"
"Is this image AI-generated?"
"Who created /Users/you/Downloads/image.png?"
Browse Directories
"What images are in my Desktop?"
"Check my Downloads for Content Credentials"
"Find AI-generated images in my Pictures"
Check URLs
"Check credentials at https://example.com/image.jpg"
How It Works
Detection Flow
1. Check Embedded C2PA Manifest (fast: ~150ms)
↓
Found? → Return immediately ✅
↓
2. Check TrustMark Watermark (slower: ~600ms)
↓
Found? → Return watermark data ✅
↓
3. Neither found → "No Content Credentials found" ❌
Why This Order?
- Performance: 80% of credentialed images have embedded manifests
- Speed: Skip expensive watermark check when not needed
- Completeness: Still catch stripped metadata via watermarks
TrustMark Watermarks
Invisible watermarks embedded in image pixels that:
- ✅ Survive JPEG compression
- ✅ Persist through social media uploads (Instagram, Twitter)
- ✅ Work after print-scan cycles
- ✅ Remain when metadata is stripped
Supported Formats
Images: JPEG, PNG, WebP, GIF, TIFF, AVIF, HEIC
Video: MP4, MOV
API Response
{
success: boolean;
hasCredentials: boolean;
// Embedded C2PA data
manifestData?: {
whoThisComesFrom?: {
linkedInIdentity?: { name, profileUrl, verified }
otherIdentities?: [{ name, socialAccounts }]
};
aboutThisContent?: {
actions?: [{ action, softwareAgent, when }]
genAIInfo?: { generative, training, model }
};
aboutTheseCredentials?: { claimSigner, timestamp };
validationInfo?: { certificate, trustInfo };
};
// Watermark data (if no embedded found)
trustMarkData?: {
identifier: string; // Watermark payload
schema: string; // BCH_SUPER, BCH_5, etc.
manifestUrl?: string; // URL to full manifest
};
error?: string;
}
Filesystem Access
Claude can browse these directories automatically:
~/Desktop~/Downloads~/Documents~/Pictures
No need to provide exact paths! Just ask:
- "What images are in my Desktop?"
- "Check recent downloads"
Development
npm run build # Compile TypeScript
npm run dev # Development mode
npm run lint # Check code quality
npm run test # Run tests
npm run precommit # Full quality check
Architecture
mcp-content-credentials/
├── src/
│ ├── index.ts # MCP server + filesystem access
│ ├── c2pa-service.ts # Detection orchestration
│ ├── trustmark-service.ts # Watermark detection (Python)
│ ├── parsers/ # Data formatters
│ └── types/ # TypeScript definitions
├── scripts/
│ ├── install-trustmark.cjs # Auto-installer
│ └── trustmark-decode.py # Python watermark decoder
└── build/ # Compiled output
Troubleshooting
"Unable to access that file"
- Restart Claude Desktop (most common fix!)
- Use absolute paths:
/Users/you/...not~/... - Verify MCP is connected: Ask "What tools do you have?"
"c2patool: command not found"
brew install contentauth/tools/c2patool # macOS
# or
npm run install-deps
"Python or TrustMark not found"
pip3 install trustmark Pillow
# or
npm run install-deps
No Content Credentials Found
This is normal! The file either:
- Wasn't created with content authentication
- Had credentials removed
- Is a screenshot/copy without provenance
Performance
- Embedded check: ~150ms (fast path, 80% of cases)
- + Watermark check: ~600ms (fallback, 20% of cases)
- First watermark: ~30s (downloads ONNX model, one-time)
Security
- ✅ Read-only filesystem access
- ✅ Limited to user directories (Desktop, Downloads, etc.)
- ✅ Input validation on all paths and URLs
- ✅ Temporary files auto-deleted
- ✅ No access to hidden/system files
Contributing
See CONTRIBUTING.md
Resources
License
MIT
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。