Build a Production-Ready Review Analytics MCP Server with TypeScript and Vector Search

Processing thousands of app store reviews manually is a bottleneck for product teams. While raw reviews contain goldmines of feedback, the noise-to-signal ratio is often too high for traditional keyword-based systems. This is where the Model Context Protocol (MCP) comes in. By building a dedicated MCP server, we can bridge the gap between unstructured review data and LLM-powered reasoning tools like Claude or GPT-4o.

In this tutorial, we will build ReviewRadar—a production-ready MCP server that leverages a hybrid architecture of regex-based rules, LLM routing, and vector search. To ensure high availability and low latency for the LLM components, we will utilize n1n.ai as our primary API gateway.

The Architecture of ReviewRadar

A robust review analytics system must be fast, cost-effective, and accurate. A purely LLM-based approach is too expensive and slow for processing thousands of reviews in real-time. Our architecture uses a tiered approach:

1. Rules Engine: Fast regex patterns to catch obvious issues (e.g., 'crash', 'login failed').
2. LLM Router: For nuanced sentiment analysis and categorization where rules fail.
3. Vector Database: To store embeddings for semantic search and duplicate detection.
4. MCP Tools: Exposing these capabilities to LLM clients for interactive querying.

Why Use an MCP Server?

MCP (Model Context Protocol) allows developers to expose local or remote data sources and tools to AI models in a standardized way. Instead of writing custom integration code for every AI feature, you build an MCP server once, and any MCP-compliant client (like Claude Desktop) can instantly use your tools. When combined with the high-speed endpoints from n1n.ai, you get a seamless bridge between your product data and advanced reasoning.

Setting Up the Project

Start by initializing a TypeScript project with the MCP SDK:

mkdir review-radar-mcp
cd review-radar-mcp
npm init -y
npm install @modelcontextprotocol/sdk zod typescript
npm install -D @types/node

Configure your tsconfig.json to support modern ESM syntax, as the MCP SDK relies heavily on it.

Implementation: The Hybrid Logic

1. The Rules Engine

We define a set of 'P0' rules. These are critical issues that must be flagged instantly without waiting for an LLM response.

const CRITICAL_KEYWORDS = [/crash/i, /won't open/i, /billing/i, /scam/i]

function checkCritical(text: string): boolean {
  return CRITICAL_KEYWORDS.some((regex) => regex.test(text))
}

2. LLM Routing via n1n.ai

For reviews that don't match simple rules, we route them to an LLM. Using n1n.ai allows us to switch between models like Claude 3.5 Sonnet and DeepSeek-V3 depending on the complexity of the review. This flexibility is key for production environments where cost and performance must be balanced.

async function classifyWithLLM(review: string) {
  const response = await fetch('https://api.n1n.ai/v1/chat/completions', {
    method: 'POST',
    headers: {
      Authorization: `Bearer ${process.env.N1N_API_KEY}`,
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      model: 'claude-3-5-sonnet',
      messages: [
        { role: 'system', content: 'Classify this review into: Bug, Feature Request, or Praise.' },
        { role: 'user', content: review },
      ],
    }),
  })
  return response.json()
}

Vector Search for Semantic Insights

To find similar reviews (e.g., 'The app is slow' vs 'Performance is laggy'), we need Vector Search. We use embeddings to represent reviews as coordinates in a multi-dimensional space.

Implementation Guide

1. Generate Embeddings: Send the review text to an embedding model (available via n1n.ai).
2. Store in Vector DB: Use Pinecone or ChromaDB to store the vector.
3. Query: When a user asks 'What are the main performance complaints?', the MCP server performs a vector similarity search.

Defining MCP Tools

Now, we expose our logic as tools that an LLM can call. In your MCP server file:

import { Server } from '@modelcontextprotocol/sdk/server/index.js'
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js'

const server = new Server(
  {
    name: 'review-radar',
    version: '1.0.0',
  },
  {
    capabilities: {
      tools: {},
    },
  }
)

server.setRequestHandler(ListToolsRequestSchema, async () => ({
  tools: [
    {
      name: 'analyze_review',
      description: 'Analyzes an app review for sentiment and critical issues',
      inputSchema: {
        type: 'object',
        properties: {
          text: { type: 'string' },
        },
        required: ['text'],
      },
    },
  ],
}))

Pro Tips for Production

1. Rate Limiting: When processing bulk reviews, ensure your MCP server handles rate limits. Using an aggregator like n1n.ai helps mitigate this by providing higher throughput across multiple model providers.
2. Caching: Implement a simple Redis cache for embeddings. If two reviews are identical, don't waste tokens re-calculating the vector.
3. Human-in-the-loop: For 'P0' issues detected by the Rules Engine, trigger a webhook to Slack or PagerDuty immediately.

Conclusion

Building an MCP server for review analytics transforms raw data into actionable intelligence. By combining the speed of TypeScript, the precision of rules-based logic, and the reasoning power of LLMs via n1n.ai, you create a system that scales with your user base.

Get a free API key at n1n.ai