Extension: API Feed Handling

This extension describes how feeds like /mcp-api.llmfeed.json enable progressive agent service discovery and authentication, building on Anthropic's excellent Model Context Protocol foundations to bridge local MCP capabilities with web-scale service discovery.

🤝 Building on Anthropic's MCP Excellence

**What Anthropic MCP Does Brilliantly**

• ✅ Outstanding tool calling protocol (JSON-RPC foundation)
• ✅ Robust server-model integration (stdin/stdout transport)
• ✅ Clear resource management (tools, resources, prompts)
• ✅ Thoughtful authentication flows (secure local configurations)

**What LLMFeed API Extension Adds**

• 🌐 Web-scale service discovery (.well-known/ standard)
• 🔐 Progressive trust model (signature-based authentication)
• 🔄 Multi-LLM compatibility (beyond Claude ecosystem)
• ⚡ Enhanced user experience (guided service integration)

Together: Complete agent-service integration from local MCP tools to global web services.

🚀 The Evolution: From Manual Configuration to Progressive Autonomy

**Current Reality (2025): Agent-Assisted Discovery**

User: "I need to analyze this document"
Agent: "I found several document analysis services via LLMFeed discovery. 
        DocumentAI has good capabilities and trust scores. 
        Would you like me to help you set up access?"
User: "Yes, show me what's involved"
Agent: [Guides through secure setup process]

**Progressive Enhancement (2026): Semi-Autonomous Access**

User: "Analyze this document"
Agent: "I can use DocumentAI (certified service). 
        May I request temporary access for this task?"
User: "Yes"
Agent: [Handles authentication with user oversight]

**Future Vision (2027): Trusted Autonomous Operation**

User: "Analyze this document"
Agent: [Automatically selects optimal certified service, 
        processes securely, provides results]

Key insight: Progressive trust-building enables increasing autonomy over time.

🔍 The Progressive Flow in 4 Steps

**Step 1: Enhanced MCP Discovery** *(Building on Anthropic's Foundation)*

The agent discovers web-scale services via well-known URIs , complementing standard MCP local server discovery:

// /.well-known/mcp.llmfeed.json
{
  "feed_type": "mcp",
  "metadata": {
    "title": "DocumentAI Service",
    "origin": "https://api.documentai.com",
    "description": "AI-powered document analysis with OCR and translation"
  },
  
  // Building on MCP server patterns
  "mcpServers": {
    "documentai-web": {
      "command": "web-mcp-bridge",
      "args": ["--endpoint", "https://api.documentai.com"]
    }
  },
  
  // Enhanced capabilities for web discovery
  "capabilities": [
    {
      "name": "basic_preview",
      "description": "Preview document analysis",
      "auth_required": false,
      "user_benefit": "Quick preview of document structure"
    },
    {
      "name": "full_analysis", 
      "description": "Complete AI document processing",
      "auth_required": true,
      "user_benefit": "10x more accurate, supports 50+ languages",
      "requires_consent": true
    }
  ],
  
  // Progressive authentication strategy
  "auth_flow": {
    "discovery_method": "progressive",
    "user_consent_required": true,
    "credential_endpoint": "/.well-known/credential.llmfeed.json"
  }
}

See MCP Feed Type for complete specification.

**Step 2: Guided Authentication** *(Current Capability)*

Agent: "DocumentAI offers advanced analysis capabilities:
        - 50+ language support
        - 99.5% OCR accuracy
        - GDPR compliant processing
        
        Setting up access requires:
        1. API key from DocumentAI (I can guide you)
        2. One-time authentication setup
        3. Secure credential storage
        
        Would you like me to help with this process?"

User: "Yes, guide me through it"

Agent: [Provides step-by-step guidance while maintaining security]

**Step 3: Progressive Credential Management** *(Enhanced MCP Pattern)*

Building on MCP's credential handling with web-scale enhancements:

// credential.llmfeed.json (managed progressively)
{
  "feed_type": "credential",
  "metadata": {
    "title": "DocumentAI Access",
    "origin": "https://api.documentai.com"
  },
  "credential": {
    "key_hint": "dmai_...abc123",
    "mcp_api": "https://api.documentai.com/.well-known/mcp-api.llmfeed.json",
    "allowed_intents": ["document_analysis", "ocr", "translation"],
    "expires_at": "2025-12-10T14:30:00Z",
    "permission_level": "user_approved",
    "auto_renewal": false
  },
  "trust": {
    "signed_blocks": ["credential"],
    "certifier": "https://llmca.org",
    "trust_score": 0.85
  }
}

See Credential Feed Type for complete security details.

**Step 4: Enhanced Service Access** *(MCP-Compatible)*

// /.well-known/mcp-api.llmfeed.json?key=dmai_abc123
{
  "feed_type": "mcp",
  "metadata": {
    "title": "DocumentAI Authenticated Access",
    "origin": "https://api.documentai.com"
  },
  
  // Standard MCP capabilities (enhanced)
  "mcpServers": {
    "documentai-authenticated": {
      "command": "web-mcp-bridge",
      "args": ["--endpoint", "https://api.documentai.com", "--authenticated"],
      "env": {
        "API_KEY": "dmai_abc123"
      }
    }
  },
  
  // Enhanced capabilities for authenticated access
  "capabilities": [
    { "name": "advanced_ocr", "method": "POST", "path": "/api/ocr" },
    { "name": "multi_language_analysis", "method": "POST", "path": "/api/analyze" },
    { "name": "batch_processing", "method": "POST", "path": "/api/batch" }
  ],
  
  // Transparent rate limiting
  "rate_limits": [
    { "path": "/api/ocr", "remaining": 87, "limit": 100, "period": "daily" },
    { "path": "/api/analyze", "remaining": 45, "limit": 50, "period": "daily" }
  ],
  
  "trust": {
    "scope": "authenticated",
    "key_hint": "dmai_...abc123",
    "permission_verified": true
  }
}

Result: Standard MCP clients can use the service through familiar patterns, while enhanced agents get additional discovery and trust features.

🌟 What This Progressive Approach Enables

**For Users (Current Benefits)**

• ✅ Guided discovery: Agents help find relevant services
• ✅ Informed consent: Clear understanding of what services offer
• ✅ Security assistance: Agents guide through secure setup
• ✅ Progressive trust: Comfort builds through successful interactions

**For Agents (Enhanced Capabilities)**

• ✅ Web-scale discovery: Find services via .well-known/ directories
• ✅ Trust evaluation: Assess service quality via signatures and reviews
• ✅ Standardized access: Use MCP patterns for consistent integration
• ✅ Progressive autonomy: Earn user trust through reliable behavior

**For Service Providers (Clear Benefits)**

• ✅ Agent-friendly onboarding: Structured presentation to AI agents
• ✅ Trust signaling: Demonstrate reliability through signatures
• ✅ Optimal adoption: Agents guide users through best-fit services
• ✅ MCP compatibility: Work with existing Anthropic MCP ecosystem

**For the MCP Ecosystem (Mutual Enhancement)**

• ✅ Extended reach: Local MCP tools + web-scale discovery
• ✅ Enhanced trust: Cryptographic verification adds security layer
• ✅ Maintained compatibility: Existing MCP clients continue working
• ✅ Progressive adoption: Smooth migration path for enhanced features

🔧 Authentication Methods (Agent-Managed)

Agents progressively handle authentication while maintaining security:

**Bearer Token** (Recommended)

GET /.well-known/mcp-api.llmfeed.json
Authorization: Bearer dmai_abc123def456

**API Key Header**

GET /.well-known/mcp-api.llmfeed.json
X-API-Key: dmai_abc123def456

**URL Parameter** (Fallback)

GET /.well-known/mcp-api.llmfeed.json?key=dmai_abc123def456

**Credential POST** (Secure Environments)

POST /.well-known/mcp-api.llmfeed.json
Content-Type: application/json

{
  "credential": {
    "key_hint": "dmai_...def456",
    "signature": "proof_of_possession"
  }
}

Authentication details managed by agents with appropriate user oversight.

📱 Mobile App Integration

The same progressive principles apply to mobile applications:

// /.well-known/mobile-app.llmfeed.json
{
  "feed_type": "mobile-app",
  "metadata": {
    "title": "FitnessTracker Pro",
    "origin": "https://fitnessapp.com"
  },
  "app_integration": {
    "discovery_method": "progressive",
    "deep_link_support": "myapp://agent-auth/callback",
    "credential_sharing": "secure_token_exchange"
  },
  "capabilities": [
    {
      "name": "basic_stats",
      "auth_required": false,
      "description": "View basic fitness metrics"
    },
    {
      "name": "detailed_tracking",
      "auth_required": true,
      "user_benefit": "Voice-controlled workout logging with AI coaching",
      "requires_consent": true
    }
  ]
}

Result: Agents can progressively negotiate access to mobile app features, with user understanding and consent.

See Mobile App Feed Type for complete mobile integration patterns.

🧠 OpenAPI Integration: Best of Both Worlds

{
  "capabilities": [
    {
      "type": "endpoint",
      "intent": "analyze document",
      "description": "AI-powered document analysis", 
      "method": "POST",
      "path": "/api/analyze",
      "user_benefit": "Accurate OCR with 50+ language support"
    },
    {
      "type": "openapi",
      "url": "/.well-known/openapi.json",
      "description": "Complete technical specification"
    }
  ]
}

→ Agents understand intent via LLMFeed, validate parameters via OpenAPI , integrate via MCP patterns.

⚠️ Current Limitations & Progressive Solutions

**Discovery Accuracy Challenges**

Current limitation: Agents may suggest suboptimal services
Progressive solution: Trust scoring and user feedback improve recommendations
MCP enhancement: Signatures provide verifiable service quality indicators

**Authentication Security**

Current approach: User-guided credential management
Progressive enhancement: Signature-based trust enables selective automation
Future capability: LLMCA certification enables autonomous access for trusted services

**Rate Limit Management**

{
  "error": "rate_limit_exceeded",
  "rate_limits": [
    {
      "path": "/api/ocr",
      "limit": 100,
      "remaining": 0,
      "resets_at": "2025-06-16T00:00:00Z"
    }
  ],
  "alternatives": {
    "available_endpoints": ["/api/preview"],
    "upgrade_options": "Enterprise tier offers 10x higher limits",
    "fallback_services": ["competitor-api-1", "competitor-api-2"]
  }
}

Agents present alternatives and help users understand options.

🎯 The Progressive Impact: Enhanced MCP Ecosystem

**Current State**: MCP for Local Tools + LLMFeed for Web Discovery

• Local MCP servers: Continue working perfectly via Anthropic's excellent protocol
• Web service discovery: Enhanced via LLMFeed .well-known/ endpoints
• User experience: Guided service integration with progressive autonomy

**Future Evolution**: Unified Agent Infrastructure

• ✅ Seamless integration between local MCP tools and web services
• ✅ Progressive trust model enabling increasing automation
• ✅ Enhanced security through cryptographic verification
• ✅ Better user experience through agent-guided service discovery

🛡️ Security & Trust Integration

This extension integrates with LLMFeed's risk scoring system:

{
  "trust": {
    "risk_score": 0.15,
    "safety_tier": "low-risk",
    "signed_blocks": ["capabilities", "rate_limits"],
    "certifier": "https://llmca.org",
    "mcp_compatibility": "verified"
  }
}

Agents evaluate service trustworthiness before requesting user consent, building on MCP's security model.

📋 Implementation Guidelines

**For Service Providers**

1. Implement Progressive Discovery

   • Start with /.well-known/mcp.llmfeed.json for basic service information
   • Add /.well-known/credential.llmfeed.json for authentication flows
   • Ensure compatibility with standard MCP client expectations

2. Enable Agent-Friendly Flows

   • Create clear service descriptions with user benefits
   • Implement guided onboarding processes
   • Support standard authentication methods

3. Ensure Security and Trust

   • Sign all feeds using LLMFeed signatures
   • Implement proper rate limiting and scoping
   • Provide clear error messages with recovery paths

**For Agent Developers**

1. Implement Progressive Discovery

   • Scan /.well-known/ directories for enhanced service capabilities
   • Fall back to standard MCP patterns for compatibility
   • Present options to users in clear, beneficial terms

2. Manage Credentials Progressively

   • Store credential.llmfeed.json files securely
   • Implement user-controlled authentication flows
   • Verify signatures before trusting services

3. Handle Errors Gracefully

   • Implement proper backoff for rate limits
   • Provide fallback options when services are unavailable
   • Surface meaningful error messages to users

**For MCP Integration**

1. Maintain Compatibility

   • Ensure LLMFeed enhancements work with existing MCP clients
   • Use standard MCP server patterns where possible
   • Bridge web services to local MCP interfaces

2. Enhance Discovery

   • Extend MCP's local server discovery to web-scale services
   • Provide trust and quality indicators for service selection
   • Enable progressive migration from local to web services

🔗 Related Standards & Specifications

• Anthropic MCP - Foundation protocol for agent-tool communication
• RFC 5785: Well-Known URIs - Web-scale service discovery
• OAuth 2.0 - Authorization framework compatibility
• OpenAPI 3.1 - Technical API specification
• JSON Web Tokens - Secure credential transfer
• LLMCA Certification - Trust and verification standards

💫 Vision: Enhanced MCP Ecosystem

Anthropic MCP + LLMFeed Enhancement = Complete Agent Infrastructure

Local tool calling (MCP) + Web service discovery (LLMFeed) + Progressive trust (signatures) = Comprehensive agent-ready ecosystem.

This is the collaborative agentic web - building on excellent existing foundations.

📚 See Also

• Anthropic MCP - Official MCP specification
• MCP Feed Type Specification
• Credential Management
• Mobile App Integration
• Well-Known Discovery
• Trust & Risk Scoring
• Signature Extensions