/.well-known/mcp Implementation Guide
RFC 8615 compliant automatic MCP server discovery
🚀 Quick Start: 30 Seconds Setup
Get automatic MCP server discovery working in under a minute
⚡ Quick Implementation
🔧 Advanced Features
Step 1: Add Discovery Link
Add one line to your existing /.mcp.json file:
{
"mcpServers": {
// ... your existing servers
},
// ✅ Add this line for automatic discovery
"llmfeed_extension": "/.well-known/mcp.llmfeed.json"
}Step 2: Create Enhanced File
Copy your MCP config to /.well-known/mcp.llmfeed.json and add:
{
"feed_type": "mcp",
"metadata": {
"title": "Your MCP Server",
"description": "Automatic discovery enabled"
},
// Copy your exact mcpServers config here
"mcpServers": { /* ... */ }
}🔧 Essential Files (RFC 8615)
/.well-known/mcp.llmfeed.jsonMain MCP configuration with enhanced discovery metadata
/.well-known/llm-index.llmfeed.jsonSemantic sitemap for automatic agent navigation
/.well-known/public.pemEd25519 public key for cryptographic verification
🌐 Explore Complete Implementation
See a real /.well-known/mcp directory with 24+ files, trust indicators, and interactive preview.
Browse Live Directory📋 Copy Working Examples
Download working MCP configurations ready for copy-paste implementation.
✅ How It Works
.mcp.json if you have one llmfeed_extension linkValidation: Successfully tested with Claude Desktop - automatic detection with user consent for security.
🎯 Trust Levels & Autonomy
💡 The Breakthrough
LLMFeed's cryptographic trust system solves the core AI autonomy challenge - enabling safe autonomous agent behavior while maintaining security. This is the only viable path from "AI assistant requiring permission" to "autonomous AI agent."
🧠 Pro Tip: Transform any LLM into an expert LLMFeed consultant by feeding it our complete training materials at /train — it becomes instantly knowledgeable about MCP discovery, trust systems, and implementation patterns.
Download Template
📚 Research Project
LLMFeed is experimental research extending MCP. For official docs visit modelcontextprotocol.io. Semi-automatic discovery tested June 2025 with user consent.