Google Trends MCP Server

Search Trends Explorer

This project combines a Google Trends visualizer built with Next.js and a Model Context Protocol (MCP) server that exposes the same data as a tool you can connect to OpenAI or any MCP-compatible client. Both experiences call SearchAPI.io to retrieve the last 12 months of relative interest for a search term.

Prerequisites

Node.js 18.18+ (recommended to avoid engine warnings)
A SEARCH_API_KEY from SearchAPI.io

Create a .env file with:

SEARCH_API_KEY=your_key_here

Running the Next.js UI

npm install npm run dev

Visit http://localhost:3000 to use the web interface.

Running the MCP Server

The MCP server exposes a single tool named fetch_google_trends over streamable HTTP.

npm install npm run mcp:dev

The server listens on http://localhost:3000/mcp (honors the PORT env variable) and also serves GET /healthz for Render health checks.

Connecting from an MCP client

MCP Inspector: npx @modelcontextprotocol/inspector --server http://localhost:3000/mcp
OpenAI / other agents: add an HTTP MCP tool pointing to the same /mcp endpoint.

When invoked, fetch_google_trends expects a JSON payload:

{ "term": "electric cars", "geo": "US", // optional Google Trends geo code "category": "0" // optional Google Trends category id }

The tool responds with structured JSON containing the normalized timeline points.

Deploying the MCP server to Render.com

Create a new Web Service from this repository.
Set the start command to:
npm run mcp:start
Add the environment variable SEARCH_API_KEY.
(Optional) Configure Render health checks to hit /healthz.

Render will inject PORT, which the server automatically respects.

Testing & Linting

npm run lint

Project Structure Highlights

app/ – Next.js app router with UI and /api/trends endpoint.
lib/trends.ts – Shared SearchAPI client used by both the API route and MCP tool.
mcp/server.ts – Express + MCP server exposing the fetch_google_trends tool.