How to Build a Local MCP Server to Index WordPress or Laravel Sites for AI Chatbots

Imagine your WordPress or Laravel site could talk. With the Model Context Protocol (MCP), that’s almost possible. This guide walks through creating a local MCP server that indexes your site’s data and powers an intelligent chatbot capable of answering questions about your content.

Understanding MCP and Why It Matters

MCP (Model Context Protocol) is a new standard that lets language models interact directly with external data sources — like your website, database, or API. It acts as a secure bridge, allowing your chatbot to retrieve real, up-to-date content rather than relying only on what it was trained on.

Step 1: Setup Your Local MCP Server

You can build a lightweight MCP server using Node.js and the official @modelcontextprotocol/sdk. Here’s the concept:

import express from 'express';
import { createServer } from '@modelcontextprotocol/sdk';

const app = express();
app.use(express.json());

const mcp = createServer({
  name: 'WordPress Knowledge Server',
  version: '0.1',
  resources: {
    wpPosts: {
      description: 'List of posts from WordPress',
      list: async () => await getWpPosts(),
    },
  },
  tools: {
    searchPosts: {
      description: 'Search posts by keyword',
      execute: async (args) => await searchIndex(args.query),
    },
  },
});

app.use('/mcp', mcp.router);
app.listen(3001, () => console.log('MCP server running on port 3001'));

Step 2: Indexing Your WordPress or Laravel Data

Use APIs or direct database connections to fetch content:

• WordPress REST API: /wp-json/wp/v2/posts
• Laravel API or Database: Connect to MySQL and extract post or product data.

Then use tools like LangChain and Chroma to store embeddings in a local vector database:

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma

texts = [("Title A", "Post content A"), ("Title B", "Post content B")]
docs = [f"{title}\\n\\n{content}" for title, content in texts]

splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
splits = splitter.create_documents(docs)
store = Chroma.from_documents(splits, OpenAIEmbeddings(), persist_directory="./chroma_db")

Step 3: Connecting the Agent

Now, link your local MCP to an OpenAI assistant or other LLM agent. The model can retrieve context from your indexed data to produce accurate, content-aware answers.

import OpenAI from "openai";
const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

const assistant = await client.beta.assistants.create({
  name: "WordPress Knowledge Agent",
  model: "gpt-4.1",
  tools: [{ type: "mcp", server_url: "http://localhost:3001/mcp" }],
});

Step 4: Automate and Enhance

Set up cron jobs or webhooks to reindex content automatically when posts or products are updated. Over time, enrich your index with metadata like authors, categories, and tags for better retrieval quality.

Architecture Summary

Final Thoughts

By combining MCP with vector search and a language model, you can build a chatbot that truly understands your content. Whether you run a WordPress blog or a Laravel-powered SaaS, this architecture lets your AI agent answer questions based on your actual data — not just what it remembers from training.