Skip to main content

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:

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​

💫 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​