MCP Server Capabilities Explained

From tool exposure to autonomous agent orchestration

🔧 MCP Servers (Anthropic)

Tools that agents can call:

• Manual tool configuration
• Local JSON-RPC protocol
• Human oversight required
• One-time tool execution

🚀 Capabilities (Enhanced)

Actions that agents orchestrate:

• Automatic capability discovery
• Web-native REST endpoints
• Autonomous workflow execution
• Multi-step orchestration

💡 Evolution: From "tools agents can use" → "actions agents can orchestrate"

📚 Complete Guide

MCP Servers: The Foundation (Anthropic)

🔧 What MCP Servers Provide

MCP servers expose tools, resources, and prompts that AI models can use through a standardized JSON-RPC protocol. Think of them as "API wrappers" that make external systems accessible to AI.

🛠️ Tools

Functions that models can call

e.g., create_file, query_database

📋 Resources

Data that models can read

e.g., file contents, API responses

💬 Prompts

Templates for common tasks

e.g., code review, data analysis

📋 Standard MCP Server Configuration🛡️ Template

Replace {{your-tokens}} and {{your-endpoints}} with actual values

// Typical MCP Server Configuration
{
  "mcpServers": {
    "github-server": {
      "command": "npx",
      "args": ["@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "{{your-github-token}}"
      }
    },
    "database-server": {
      "command": "python",
      "args": ["-m", "mcp_server_database"],
      "env": {
        "DATABASE_URL": "postgresql://{{your-db-connection}}"
      }
    }
  }
}


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✅ What this enables: Claude can create GitHub issues and query databases through structured tool calls.

Tools vs Capabilities: The Key Distinction

MCP Tools (Current)

Single actions: One tool = one function call
Local configuration: Manual setup required
Human oversight: User approval for each action
Static definitions: Fixed tool descriptions

Enhanced Capabilities (Evolution)

Workflow orchestration: Multi-step automation
Auto-discovery: Agents find capabilities automatically
Risk-based autonomy: Smart consent based on trust
Rich metadata: Intent, risk, and behavioral guidance

Real-World Example: Booking an Appointment

🔧 MCP Tools Approach

User: "Book me a doctor's appointment"

Agent: "I can help you with that. Let me use the booking tool..."

1. Calls book_appointment tool
2. User provides details manually
3. Tool creates appointment
4. Returns confirmation
5. User manually adds to calendar
6. User sets own reminders

Result: 1 automated step, 5+ manual steps

🚀 Capabilities Approach

User: "Book me a doctor's appointment"

Agent: "Done! You're booked for Tuesday 2PM. Details sent to your email."

1. Discovers booking capabilities
2. Checks available times automatically
3. Books optimal appointment
4. Adds to calendar automatically
5. Sets reminders automatically
6. Sends confirmation email

Result: Complete workflow automation

The Web Automation Challenge

🚧 Current Limitations of MCP Tools

Discovery Problem

• No automatic discovery mechanism
• Manual configuration required
• Limited to pre-configured tools
• Can't adapt to new services

Orchestration Problem

• Each tool call requires approval
• No workflow automation
• Limited cross-tool coordination
• High user friction

💡 What Users Actually Want

Natural Intent Expression

"Book me a flight to Paris next week and find a hotel near the Louvre"

Autonomous Execution

Agent should discover services, compare options, and execute bookings automatically

Intelligent Coordination

Coordinate flight times with hotel check-in, add to calendar, set reminders

🎯 The Gap

MCP tools provide the building blocks, but capabilities provide the intelligent orchestrationthat users actually want.

Capability Auto-Discovery: How It Works

🔍 Discovery Process

Agent encounters service

User mentions "book a flight" → Agent identifies airline websites

Discovers capabilities

Checks /.well-known/capabilities.llmfeed.json for available actions

Evaluates trust & risk

Checks signatures, risk scores, and required consent levels

Orchestrates workflow

Combines multiple capabilities into intelligent automation

📋 Capabilities Feed Structure🛡️ Template

Template only - Replace {{your-endpoints}} with actual API paths

{
  "feed_type": "capabilities",
  "metadata": {
    "title": "AirlineBooking Pro Capabilities",
    "origin": "https://demo-airline.example",
    "description": "Flight booking and travel automation"
  },
  "capabilities": [
    {
      "name": "searchFlights",
      "method": "GET",
      "path": "/api/{{flights-search-endpoint}}",
      "description": "Search available flights with preferences",
      "input_schema": {
        "required": ["origin", "destination", "departure_date"],
        "optional": ["return_date", "passengers", "class"]
      },
      "requires_user_consent": false,
      "trust_level_required": "basic",
      "risk_score": 0.2,

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✅ Key innovation: Risk-based consent, workflow orchestration, and trust verification built-in.

Migration: From MCP Tools to Enhanced Capabilities

🔄 Migration Strategy

Transform your existing MCP tools into web-discoverable capabilities while maintaining 100% backward compatibility.

Keep Your MCP Tools (Zero Changes)

Your existing local MCP configuration continues working unchanged.

📋 Existing MCP Configuration🛡️ Template

Your existing setup - no changes needed

// Your existing MCP tools keep working
{
  "mcpServers": {
    "github-server": { /* unchanged */ },
    "database-server": { /* unchanged */ }
  }
}

Create Web-Discoverable Capabilities

Transform tools into web-accessible capabilities with enhanced metadata.

📋 Web-Discoverable Capabilities Migration🛡️ Template

Replace {{your-domain}} and {{your-endpoints}} with actual values

// /.well-known/capabilities.llmfeed.json
{
  "feed_type": "capabilities",
  "metadata": {
    "title": "My Development Tools",
    "origin": "https://{{your-domain.com}}"
  },
  "capabilities": [
    {
      "name": "createGithubIssue",
      "method": "POST",
      "path": "/api/{{github-integration}}/issues",
      "description": "Create GitHub issue with smart categorization",
      "requires_user_consent": false,
      "risk_score": 0.3,
      "audience": ["llm", "dev_agent"]
    }
  ]
}

Add Enhanced Features

Risk scoring, workflow orchestration, and trust verification.

Risk-based consentWorkflow chainingCryptographic trust

🌐 Live Example: WellKnownMCP Implementation

Here's our actual production capabilities feed that powers wellknownmcp.org tools. This content is indexable by search engines as a reference implementation.

🌐 WellKnownMCP Production Capabilities🔍 Live

Live production content from wellknownmcp.org

{
  "feed_type": "capabilities", 
  "metadata": {
    "title": "WellKnownMCP Tools & Analyzers",
    "origin": "https://wellknownmcp.org",
    "description": "LLMFeed validation, analysis, and development tools",
    "generated_at": "2025-06-22T12:00:00Z"
  },
  "trust": {
    "signed_blocks": ["metadata", "capabilities", "trust"],
    "algorithm": "ed25519",
    "certifier": "https://llmca.org",
    "scope": "public"
  },
  "capabilities": [
    {
      "name": "validateFeed",
      "method": "POST",
      "path": "/api/validate",
      "description": "Validate LLMFeed JSON structure and signatures",

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🔍 IndexableThis content helps search engines understand our live capabilities

🛠️ Development Tools

📚 Resources

Ready to Build Agent-Orchestrated Experiences?

Transform your MCP tools into intelligent capabilities that agents can discover and orchestrate autonomously.

Learn MCP Basics Live Example Implementation Guide

🎯 Start Your Journey

Join the capability revolution and help build the intelligent web that agents can orchestrate autonomously.

📚 Acknowledgment & Standards

MCP (Model Context Protocol) is developed by Anthropic. Official documentation at modelcontextprotocol.io. Capabilities feeds extend MCP with web-native discovery, intelligent orchestration, and trust-based autonomy. This represents our research into the evolution from manual tools to autonomous agent workflows.