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The Model Context Protocol (MCP) is an open standard that defines how AI models and agents can discover and use tools. Noorle uses MCP as its foundational protocol for exposing capabilities.

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What is MCP?

MCP is a specification developed by Anthropic that standardizes the interface between AI models and tools. Instead of each AI provider or tool implementing their own integration formats, MCP provides a unified language. Think of it like HTTP for AI tools.
  • HTTP: Standardized how web clients talk to servers
  • MCP: Standardizes how AI agents discover and invoke tools

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Why MCP Matters

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Before MCP

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With MCP

Now:
  • Agents use one unified interface for all tools
  • New tools are discovered automatically
  • Error handling is consistent
  • Tools can be swapped without agent changes

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MCP Primitives

MCP defines three core primitives that tools expose:

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1. Tools

Callable functions with typed inputs and outputs. 
{
  "name": "search_github",
  "description": "Search GitHub repositories",
  "inputSchema": {
    "type": "object",
    "properties": {
      "query": {
        "type": "string",
        "description": "Search query"
      },
      "language": {
        "type": "string",
        "description": "Programming language filter (optional)"
      }
    },
    "required": ["query"]
  }
}
Key aspects:
  • Name: MCP-compatible identifier (alphanumeric + underscore)
  • Description: Human-readable purpose
  • Input Schema: JSON Schema defining what parameters it accepts
  • Output: Structured response (JSON)

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2. Resources

Read-only data that tools can reference. 
{
  "uri": "memory://conversation-history",
  "name": "Conversation History",
  "description": "Current conversation with user",
  "mimeType": "text/plain"
}
Use cases:
  • Conversation memory
  • Document references
  • Configuration data
  • Read-only knowledge bases

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3. Prompts

Reusable prompt templates that guide model behavior. 
{
  "name": "analyze-sentiment",
  "description": "Analyze sentiment of user message",
  "arguments": [
    {
      "name": "text",
      "description": "Text to analyze"
    }
  ]
}
Benefits:
  • Standardize prompt engineering across tools
  • Document best practices for tool usage
  • Enable tool-specific optimization hints

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How Noorle Uses MCP

Noorle exposes all capabilities (builtin, plugin, connector) through MCP:

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MCP Transport

MCP is transport-agnostic. Noorle supports:

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Server-Sent Events (SSE)

Recommended for web clients and long-lived connections. Advantages:
  • Native HTTP/REST
  • Works through proxies and load balancers
  • Browser-compatible
  • Simpler than WebSocket

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HTTP Long-Polling

Fallback for restricted networks. Advantages:
  • Works in highly restricted environments
  • No special proxying needed
Noorle uses SSE by default with long-polling fallback for maximum compatibility.

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Tool Discovery via MCP

When a client connects to an MCP gateway, it discovers available tools: 
const gateway = new NoorleMcpClient({
  url: "https://mcp-{id}.noorle.com",
  auth: { token: "jwt-token" }
});

// List all tools
const tools = await gateway.listTools();

// Response
[
  {
    name: "web_search",
    description: "Search the web with Bing",
    inputSchema: {
      type: "object",
      properties: {
        query: { type: "string" }
      },
      required: ["query"]
    }
  },
  // ... more tools
]

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Tool Invocation via MCP

Once discovered, tools are called through a standard interface: 
const result = await gateway.callTool("web_search", {
  query: "latest AI trends"
});

// Response
{
  "success": true,
  "data": {
    "results": [
      {
        "title": "...",
        "url": "...",
        "snippet": "..."
      }
    ]
  }
}

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MCP vs REST APIs

AspectMCPREST API
DiscoveryAutomatic (listTools)Manual reading docs
SchemaStandardized JSON SchemaEach API different
Error HandlingConsistent formatAPI-specific
AuthenticationJWT or API keyVarious (OAuth, API key, etc.)
StreamingFirst-class supportAdd-on (webhooks, polling)
TL;DR: MCP is REST for AI tools.

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MCP Ecosystem

Noorle is part of the broader MCP ecosystem:
  • MCP Servers: Tools that expose capabilities (Noorle, Anthropic plugins, community servers)
  • MCP Clients: AI agents that consume tools (Claude, custom agents, integrations)
  • MCP Registry: Directory of public servers (https://mcp-registry.io)

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Omni Tool: Smart Tool Discovery

Rather than listing 50+ tools and expecting the AI to choose, Noorle’s Omni Tool uses AI to discover the right tool: The agent says “find top product” once, and Omni Tool maps that to the right tool automatically. See Omni Tool for details.

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Key Takeaways

  • MCP is the standard for AI tool integration
  • Noorle uses MCP to expose all capabilities uniformly
  • No tool lock-in: Your tools work with any MCP client
  • Automatic discovery: Clients learn available tools on connection
  • Consistent interface: All tools follow same pattern regardless of type

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Next: Gateways

Now that you understand MCP, see how MCP Gateways expose these tools to clients.