MCP Google Trends Server
Provides access to Google Trends search interest data with backtesting support, allowing retrieval of historical 30-day trend windows ending at specified cutoff dates for forecasting applications without future information leakage.
README
MCP Google Trends Server
MCP server providing Google Trends search interest data with backtesting support via cutoff date filtering.
Overview
This server provides a tool to retrieve Google Trends data for any search query over a 30-day period ending at a specified cutoff date. It's designed for forecasting applications that require historical search interest data without future information leakage.
Features
- Backtesting Compliant: All data is strictly filtered to before the cutoff date
- 30-Day Windows: Retrieves search interest trends for the 30 days before cutoff
- US Region: Currently configured for US search interest data
- 0-100 Scale: Returns normalized interest scores where 100 = peak popularity
Tools
get_google_trends
Get Google Trends data for a query over the past 30 days before a cutoff date.
Parameters:
query(str): The search query to analyze trends forcutoff_date(str): ISO format date (YYYY-MM-DD) - retrieve trends data ending at this date
Returns:
- Formatted string with:
- Query term
- Time period (30 days before cutoff_date to cutoff_date)
- Region (US)
- Interest over time data (0-100 scale)
- Marks partial data entries
Example:
result = await get_google_trends(
query="artificial intelligence",
cutoff_date="2024-06-01"
)
# Returns trends from 2024-05-02 to 2024-06-01
Environment Variables
Required:
SERPAPI_API_KEY: API key for SerpAPI Google Trends access
Get your API key at: https://serpapi.com/
Installation
cd mcp-google-trends
uv sync
Usage
Testing Locally
mcp run -t sse google_trends_server.py:mcp
As Git Submodule
git submodule add <repo-url> mcp-servers/mcp-google-trends
Backtesting Compliance
This server is designed for use in forecasting applications that require strict temporal boundaries:
- Date Range Enforcement: The 30-day window is calculated from the cutoff_date backwards
- API-Level Filtering: Date constraints are passed directly to SerpAPI's Google Trends engine
- No Future Data: All returned data points are guaranteed to be from before the cutoff_date
This makes it safe to use in backtesting scenarios where you're simulating predictions made at historical points in time.
Testing
Setup
- Install test dependencies:
uv pip install -e ".[test]"
- Configure environment variables by copying
.env.exampleto.env:
cp .env.example .env
- Add your API key to
.env:SERPAPI_API_KEY- Required from https://serpapi.com/
Running Tests
Run all tests:
pytest
Run with verbose output:
pytest -v
Run specific test file:
pytest tests/test_google_trends_server.py
Run specific test:
pytest tests/test_google_trends_server.py::test_get_google_trends_basic -v
Test Coverage
The test suite covers:
- Basic functionality: Standard Google Trends queries with various topics
- Date handling: Different cutoff dates (recent, historical, future, invalid)
- Query types: Single words, multi-word phrases, brands, special characters, numeric queries
- Output format: Verification of all expected sections (header, period, region, data points)
- Date range: Validation of 30-day lookback window
- Error handling: Invalid date formats, malformed dates
- Region info: Verification of US region specification
Note: Tests make real API calls to SerpAPI and require a valid SERPAPI_API_KEY. Tests will be skipped if the API key is not set. API rate limits and costs may apply.
Dependencies
- fastmcp: MCP server framework
- serpapi: SerpAPI Python client for Google Trends access
- python-dotenv: Environment variable management
API Details
Uses SerpAPI's Google Trends engine with the following parameters:
data_type: "TIMESERIES" - returns time-series datageo: "US" - United States regiontz: 0 - UTC timezonedate: Date range in format "{start_date} {end_date}"
Error Handling
The tool returns user-friendly error messages for common issues:
- Invalid date format (not YYYY-MM-DD)
- Missing SERPAPI_API_KEY
- API request failures
- No data found for query
All errors are returned as strings rather than raising exceptions, making integration more predictable.
Limitations
- Currently configured for US region only (can be extended to other regions)
- Fixed 30-day lookback window (can be made configurable)
- Requires active SerpAPI subscription with Google Trends access
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.