Introduction to AI-RAN and MCP

Inspired by the recent groundbreaking AI-RAN (Artificial Intelligence Radio Access Network) advancements from industry leaders like NTT DOCOMO and Samsung, the telecommunications sector is undergoing a massive transformation in 2026. The integration of artificial intelligence directly into the radio access network allows for dynamic, real-time optimization of 5G and 6G infrastructures. In this deep dive, we will architect a Model Context Protocol (MCP) server that empowers AI assistants, such as Claude Desktop and Cursor IDE, to act as autonomous network engineers. By exposing AI-RAN capabilities through MCP tools, we can enable LLMs to analyze network telemetry, predict coverage gaps, configure cell parameters, and manage spectrum allocation autonomously.

The synergy between MCP Tools and AI-RAN opens up unprecedented opportunities for automated network operations (AIOps). Instead of human operators manually adjusting antenna tilts or transmit power based on static dashboards, an AI agent connected via our MCP server can continuously ingest metrics and apply optimal configurations in milliseconds. This guide will walk you through building this server using the FastMCP TypeScript SDK, employing Zod for strict schema validation to ensure absolute safety in network configuration commands.

Understanding the AI-RAN MCP Architecture

Our AI-RAN MCP server acts as a middleman between the AI Assistant (e.g., Claude) and the telecom provider's Network Orchestrator API. The server defines a set of tools that the AI can call. These tools include reading current cell metrics (latency, throughput, active users), predicting future congestion using onboard models, and executing configuration changes.

We will define the following MCP tools:

• get_cell_telemetry: Retrieves real-time performance metrics for a specific cell tower.
• predict_coverage_gap: Analyzes historical data and current topography to predict potential dead zones.
• configure_cell_parameters: Adjusts physical and MAC layer parameters like transmit power, tilt, and beamforming weights.
• allocate_spectrum: Dynamically redistributes frequency bands across adjacent cells to balance load.

This architecture is crucial for exploring advanced AI workflows in telecommunications.

Setting Up the FastMCP TypeScript Project

First, let's initialize our project and install the necessary dependencies. We will use the FastMCP SDK, which abstracts much of the boilerplate associated with the Model Context Protocol.

mkdir ai-ran-mcp
cd ai-ran-mcp
npm init -y
npm install @modelcontextprotocol/sdk zod typescript @types/node ts-node --save
npx tsc --init

Update your tsconfig.json to ensure proper module resolution and strict typing.

Implementing the AI-RAN MCP Server

Below is the complete TypeScript implementation of our AI-RAN Network Optimization MCP Server. We use Zod to define rigorous input schemas, which is a critical safety measure when allowing AI agents to modify network infrastructure.

import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import {
  CallToolRequestSchema,
  ListToolsRequestSchema,
} from "@modelcontextprotocol/sdk/types.js";
import { z } from "zod";
// Zod Input Schemas for robust validation
const GetCellTelemetrySchema = z.object({
cellId: z.string().describe("The unique identifier of the cell tower (e.g., CELL-5G-1024)"),
durationMinutes: z.number().min(1).max(60).describe("Duration of telemetry history to retrieve"),
});
const PredictCoverageGapSchema = z.object({
regionId: z.string().describe("Geographic region ID to analyze"),
trafficLoadForecast: z.number().min(0).max(100).describe("Forecasted traffic load percentage"),
});
const ConfigureCellParametersSchema = z.object({
cellId: z.string(),
transmitPowerDbm: z.number().min(20).max(46).describe("Transmit power in dBm"),
antennaTiltDegrees: z.number().min(-10).max(10).describe("Electrical antenna tilt in degrees"),
beamformingMode: z.enum(["SU-MIMO", "MU-MIMO", "Massive-MIMO"]),
});
const AllocateSpectrumSchema = z.object({
sourceCellId: z.string(),
targetCellId: z.string(),
bandwidthMhz: z.number().multipleOf(5).min(5).max(100),
frequencyBand: z.string().describe("e.g., n78, n258"),
});
// Initialize the MCP Server
const server = new Server(
{
name: "ai-ran-optimizer",
version: "1.0.0",
},
{
capabilities: {
tools: {},
},
}
);
// Register Tools
server.setRequestHandler(ListToolsRequestSchema, async () => {
return {
tools: [
{
name: "get_cell_telemetry",
description: "Retrieve real-time and historical performance metrics for a 5G/6G cell.",
inputSchema: zodToJsonSchema(GetCellTelemetrySchema),
},
{
name: "predict_coverage_gap",
description: "Predict network dead zones based on traffic load and regional data.",
inputSchema: zodToJsonSchema(PredictCoverageGapSchema),
},
{
name: "configure_cell_parameters",
description: "Autonomously adjust physical layer parameters of a cell.",
inputSchema: zodToJsonSchema(ConfigureCellParametersSchema),
},
{
name: "allocate_spectrum",
description: "Dynamically shift spectrum allocation between adjacent cells.",
inputSchema: zodToJsonSchema(AllocateSpectrumSchema),
},
],
};
});
// Handle Tool Executions
server.setRequestHandler(CallToolRequestSchema, async (request) => {
const { name, arguments: args } = request.params;
try {
switch (name) {
case "get_cell_telemetry": {
const parsed = GetCellTelemetrySchema.parse(args);
// Mock API call to network orchestrator
const telemetry = {
latencyMs: 12.5,
throughputGbps: 1.2,
activeUeCount: 450,
status: "Optimal",
};
return {
content: [{ type: "text", text: JSON.stringify(telemetry, null, 2) }],
};
}
  case "predict_coverage_gap": {
    const parsed = PredictCoverageGapSchema.parse(args);
    // Mock prediction logic
    const prediction = {
      gapDetected: parsed.trafficLoadForecast > 85,
      severity: parsed.trafficLoadForecast > 85 ? "High" : "Low",
      affectedAreaCoordinates: "35.6895° N, 139.6917° E",
    };
    return {
      content: [{ type: "text", text: JSON.stringify(prediction, null, 2) }],
    };
  }

  case "configure_cell_parameters": {
    const parsed = ConfigureCellParametersSchema.parse(args);
    // Mock configuration application
    return {
      content: [{ type: "text", text: `Successfully configured cell ${parsed.cellId} with Tx Power ${parsed.transmitPowerDbm}dBm and mode ${parsed.beamformingMode}.` }],
    };
  }

  case "allocate_spectrum": {
    const parsed = AllocateSpectrumSchema.parse(args);
    return {
      content: [{ type: "text", text: `Transferred ${parsed.bandwidthMhz}MHz of ${parsed.frequencyBand} from ${parsed.sourceCellId} to ${parsed.targetCellId}.` }],
    };
  }

  default:
    throw new Error(`Tool not found: ${name}`);
}

} catch (error) {
return {
isError: true,
content: [{ type: "text", text: error.message }],
};
}
});
// Helper for Zod to JSON Schema conversion (simplified for brevity)
import { zodToJsonSchema } from "zod-to-json-schema";
// Start Server
async function main() {
const transport = new StdioServerTransport();
await server.connect(transport);
console.log("AI-RAN MCP Server running on stdio");
}
main().catch(console.error);

OAuth 2.0 & API Key Security Guide

When deploying an MCP server that modifies critical telecommunications infrastructure, security is paramount. You must not expose these endpoints without strict authentication and authorization. We recommend integrating OAuth 2.0 with a Zero Trust architecture.

To secure the MCP server:

1. API Key Injection: Pass the Telecom API Key via environment variables to the MCP server. Never hardcode credentials.
2. OAuth 2.0 Flow: If the network orchestrator requires user-delegated access, implement an OAuth 2.0 device authorization grant flow within the MCP server initialization phase.
3. Role-Based Access Control (RBAC): Ensure the API key used by the MCP server is scoped strictly to the cells it is permitted to manage. For instance, restrict the configure_cell_parameters tool to only accept cellIds within a pre-approved array.

mcpServers Configuration for Claude Desktop & Cursor IDE

To use this server, you need to register it in your AI client's configuration file.

For Claude Desktop

Edit your claude_desktop_config.json:

{
  "mcpServers": {
    "ai-ran-optimizer": {
      "command": "node",
      "args": ["/path/to/ai-ran-mcp/build/index.js"],
      "env": {
        "TELECOM_API_KEY": "your-secure-api-key"
      }
    }
  }
}

<h3>For Cursor IDE</h3>
<p>In Cursor, navigate to <strong>Cursor Settings &gt; Features &gt; MCP Servers</strong>. Add a new server with the following details:</p>
<ul>
  <li><strong>Name:</strong> ai-ran-optimizer</li>
  <li><strong>Type:</strong> command</li>
  <li><strong>Command:</strong> <code>node /path/to/ai-ran-mcp/build/index.js</code></li>
</ul>
<p>Now, you can ask Cursor's Chat to "Analyze the telemetry for CELL-5G-1024 and adjust the transmit power if traffic load exceeds 80%."</p>

Exploring the Future of AIOps

By connecting Claude Desktop to telecom infrastructure, we transition from reactive dashboards to proactive, agentic workflows. For more insights on building autonomous systems, check out our latest AI news on AI-RAN breakthroughs.