ChatGPT Book MCP Connector
Enables ChatGPT to search for books via Algolia or mock data, returning rich book details.
README
ChatGPT Book MCP Connector
A clean and simple monorepo containing a TypeScript Model Context Protocol (MCP) server and a local React testing UI.
This connector exposes a search_product tool that allows ChatGPT to search for books. ChatGPT connects to the server using Server-Sent Events (SSE) via the /mcp endpoint. When a search runs, the backend queries Algolia (or uses high-quality local mock data as a fallback), returning rich details for books (image, name, edition, author, purchase/details link, and format options). The React UI allows developers to preview how the book cards and grids will render inside ChatGPT, inspect raw JSON tool outputs, and test search functionality.
Folder Structure
chatgpt-book-mcp/
├── package.json # Root monorepo configuration (npm workspaces)
├── README.md # This documentation file
├── mcp-server/ # Express-based SSE MCP Backend (TypeScript)
│ ├── package.json
│ ├── tsconfig.json
│ ├── src/
│ │ ├── index.ts # SSE server & API gateway
│ │ ├── algolia.ts # Algolia Integration & mock search fallback
│ │ └── types.ts # Shareable book interfaces
│ └── scripts/
│ └── test-tool.ts # CLI tool for testing the search tool
└── react-ui/ # React Front-end Dashboard (TypeScript/Vite)
├── package.json
├── tsconfig.json
├── vite.config.ts
├── index.html
└── src/
├── main.tsx
├── App.tsx # Interactive developer sandbox dashboard
├── index.css # Sleek modern styling (Vanilla CSS)
├── components/ # Beautiful responsive React widgets
│ ├── SearchBar.tsx
│ ├── ProductCard.tsx
│ ├── ProductGrid.tsx
│ └── RawJsonView.tsx
└── services/
└── mcpClient.ts # Client helper to fetch search results
Quick Start
1. Prerequisites
Ensure you have Node.js (v18 or higher) and npm installed.
2. Install Dependencies
Run the following command in the root of the monorepo:
npm install
3. Set Up Environment Variables
Inside the mcp-server directory, create a .env file (you can copy .env.example as a template):
cp mcp-server/.env.example mcp-server/.env
By default, if no credentials are provided in .env, the server will gracefully fallback to returning high-fidelity Mock Book Data so that you can run and test everything out-of-the-box. To connect your live books database, configure the Algolia credentials:
PORT=3000
ALGOLIA_APP_ID=your_algolia_app_id
ALGOLIA_API_KEY=your_algolia_search_only_api_key
ALGOLIA_INDEX_NAME=your_book_index_name
4. Run Development Servers
Start both the Backend SSE Server (port 3000) and Frontend React Testbed (port 5173) concurrently:
npm run dev
Testing the Setup
Method 1: Local React Sandbox Dashboard (Recommended)
Open your browser and navigate to:
http://localhost:5173
- Use the sleek search bar to query books like "Harry Potter", "Clean Code", "JavaScript", or "Design".
- Toggle the JSON Inspect panel to view the exact structure sent back by the backend tool.
- Interact with the responsive Book Grid and view the custom Book Cards with cover art, author names, editions, available formats (e.g. Hardcover, Paperback, E-book), and product links.
Method 2: Command Line Tester (MCP CLI Tool)
You can directly run a query against the MCP server tool from the command line:
npm run test-tool -- "Clean Code"
This runs mcp-server/scripts/test-tool.ts using tsx, sending the query to the tool handler and outputting the formatted JSON-RPC result in your terminal.
Integrating with ChatGPT
To register this connector as a custom application in ChatGPT:
- Deploy the
mcp-serverto a publicly accessible HTTPS endpoint (or use a local tunnel likengrokorlocaltunnelduring development:ngrok http 3000). - In ChatGPT, go to Explore GPTs -> Create a GPT -> Configure -> Actions -> Add Action (or the corresponding Custom MCP Settings).
- Provide the SSE endpoint URL. ChatGPT will initiate connection to:
https://<your-deployed-domain>/mcp - Once connected, ChatGPT can invoke the
search_producttool automatically when users ask questions like "Find books about Javascript" or "Show me editions of Harry Potter". - The JSON-RPC response returned by the server will then be formatted by ChatGPT. You can styling-guide ChatGPT using standard system instructions to list details or output the widget values.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.