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Matías J. Magni
Matías J. Magni

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Extending LLMs with Real-Time Data: A Comprehensive Guide to the Model Context Protocol (MCP)

#ai #architecture #llm #mcp

The Context Crisis in Generative AI

The rapid evolution of Large Language Models (LLMs) has revolutionized the landscape of artificial intelligence, yet these models remain constrained by a fundamental limitation: the "Knowledge Cutoff." Pre-trained models, regardless of their parameter count or reasoning capabilities, operate within a frozen temporal window. They are disconnected from live data streams, private databases, and the immediate physical state of the systems they inhabit.

Traditional solutions, such as Retrieval-Augmented Generation (RAG) or manual context injection, often prove brittle, slow, or insufficient for real-time agentic workflows where latency and data freshness are paramount.

This disconnect manifests starkly in professional environments. An LLM trained in 2023 cannot tell a developer why their server is lagging right now, nor can it provide the current trading price of a volatile cryptocurrency. Without external connectivity, the model attempts to answer these queries based on probabilistic patterns learned during training, leading to confident hallucinations.

The MCP Solution: A Universal Standard

The Model Context Protocol (MCP) addresses these fragmentation issues by defining a universal standard for how AI models interact with tools and data. Described conceptually as the "USB-C of AI," MCP provides a single, standardized port that allows any "peripheral" (server) to connect to any "computer" (AI client). Just as a USB-C drive works with a laptop regardless of the manufacturer, an MCP server works with any MCP-compliant host (like Claude Desktop or Cursor IDE) without custom glue code.

This standardization occurs at two critical levels:

  1. Transport Layer: Supporting standard input/output (stdio) for local, secure execution, and Server-Sent Events (SSE) for remote connections. The stdio transport is particularly significant for local development, as it ensures that sensitive API keys remain entirely on the user's machine.
  2. Protocol Layer: Utilizing JSON-RPC 2.0 to define message structures for requests, responses, and notifications.

Architectural Primitives of MCP

To build effective MCP servers, one must understand the three core primitives that the protocol exposes to the LLM:

  • Tools (Action): Executable functions exposed by the server. They represent the "hands" of the agent. For example, get_crypto_price(coin: "solana").
  • Resources (Reading): Passive data sources like file handles or URLs. They provide context that can be loaded on demand, identified by URI schemes like crypto://watchlist.
  • Prompts (Guidance): Pre-configured templates that help users accomplish specific tasks (e.g., an analyze_market prompt that pre-loads context).

Case Study I: Financial Intelligence with TypeScript

The first implementation focuses on a Crypto Tracker built using TypeScript. This demonstrates how to leverage the extensive JavaScript ecosystem and the official MCP SDK. You can find the source code for this implementation here: BugMentor/mcp-crypto.

The Tool Definition

Using Zod, we can create a schema that acts as the single source of truth for both runtime validation and the JSON schemas required by LLMs.

// server.ts implementation pattern
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

// Initialize the server instance
const server = new McpServer({
  name: "crypto-tracker",
  version: "1.0.0"
});

// Tool Definition using Zod for schema validation
server.tool(
  "get_crypto_price",
  // Zod schema defines the expected input for the LLM
  { crypto_id: z.string() }, 
  async ({ crypto_id }) => {
    // Business Logic: Fetching from external CoinGecko API
    const price = await api.fetchPrice(crypto_id); 

    // Return structured text content adhering to MCP protocol
    return {
      content: [{ type: "text", text: `Current price: $${price}` }]
    };
  }
);

// Start the server using Stdio transport
const transport = new StdioServerTransport();
await server.connect(transport);
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Case Study II: Environmental Awareness with Python

The mcp-server-weather project utilizes FastMCP, a high-level framework designed for rapid development. The full implementation is available at BugMentor/mcp-server-weather.

Decorator-Based Definition

FastMCP embraces Python's dynamic nature, using decorators (@mcp.tool()) and type hints to automatically generate tool definitions.

# server.py implementation using FastMCP
from mcp.server.fastmcp import FastMCP
import httpx
import json

# Initialize the FastMCP server
mcp = FastMCP("weather")

@mcp.tool()
async def get_current_weather(latitude: float, longitude: float) -> str:
    """
    Get current weather for a location. 
    The docstring becomes the tool description for the LLM.
    """
    url = f"https://api.open-meteo.com/v1/forecast?latitude={latitude}&longitude={longitude}&current_weather=true"

    # Asynchronous HTTP request to avoid blocking the server
    async with httpx.AsyncClient() as client:
        response = await client.get(url)
        data = response.json()

    return json.dumps(data, indent=2)
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Case Study III: High-Performance System Monitoring with Rust

The mcp-server-rust-sentinel addresses a critical requirement for infrastructure agents: Zero Overhead. Rust compiles to a native binary and manages memory without a garbage collector, offering extreme performance. The complete Rust source code can be found at BugMentor/mcp-server-rust-sentinel.

Implementation Logic

The server must define structs that mirror the JSON-RPC 2.0 specification.

// Rust struct definition for JSON-RPC responses
struct JsonRpcResponse {
    jsonrpc: String,
    // The ID matches the request ID to ensure async correlation
    id: Option<Value>,
    // Rust's Option type handles nullability safely
    #[serde(skip_serializing_if = "Option::is_none")]
    result: Option<Value>,
    #[serde(skip_serializing_if = "Option::is_none")]
    error: Option<Value>,
}
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This interaction demonstrates true agentic capability: the model perceives a problem (slowness), formulates a diagnostic plan (check stats, then check processes), executes it via Rust, and synthesizes a conclusion based on real-time data.

Integration and Orchestration

Currently, Claude Desktop serves as the primary host environment. To register custom servers, users modify the claude_desktop_config.json file.

{
  "mcpServers": {
    "weather": {
      "command": "uv",
      "args": ["run", "server.py"],
      "cwd": "C:/Users/dev/documents/mcp-server-weather"
    },
    "crypto-tracker": {
      "command": "npx",
      "args": ["-y", "tsx", "src/index.ts"],
      "cwd": "C:/Users/dev/documents/mcp-crypto"
    },
    "rust-sentinel": {
      "command": "C:/Users/dev/documents/mcp-server-rust-sentinel/target/release/mcp-server-rust-sentinel.exe",
      "args": []
    }
  }
}
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Note: The configuration strictly requires absolute paths to the executables and working directories (cwd).

Technical Appendix: Comparison

Feature TypeScript (mcp-crypto) Python (mcp-server-weather) Rust (rust-sentinel)
Framework MCP SDK + Zod FastMCP Serde + Sysinfo
Type Safety High (Compile time via Zod) High (Runtime hints) Extreme (Compile time)
Performance Moderate (Node.js runtime) Moderate (Python VM) High (Native Binary)
Best Use Case Web APIs, JSON heavy tasks Data Science, Scripts System Tools, Daemons

Conclusion

The Model Context Protocol represents a maturing of the Generative AI stack. By decoupling the model's reasoning capabilities from its context window and execution environment, MCP enables a modular, scalable, and secure approach to building intelligent systems.

Whether tracking crypto markets, checking the weather, or monitoring server health, MCP provides the missing link that turns a text generator into a functional digital assistant.

References

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