HasData Google Trends

Google Trends MCP Server

A hosted Google Trends MCP server that gives Claude, Cursor, Windsurf and any other MCP client one Google Trends tool. Pull interest over time, interest by region, and the rising and top related queries and topics for any term, all as structured JSON, with no scraping library to keep alive and no Google account.

1,000 free credits every month, no card required, which is 100 Google Trends calls.

https://mcp.hasdata.com/mcp?apis=google_trends

• Prompts and resources

Contents

• What you need
• Quick start
• Example prompts
• Tools
• Errors and failure paths
• Pricing, free tier and limits
• Tool selection
• How it compares
• FAQ
• HasData links
• Development
• Contributing
• License

What you need

An MCP client and a HasData API key from the dashboard, free to create with no card, and the free tier covers about 100 calls a month at the 10-credit rate. This is a remote server, so the simplest path is a URL and an x-api-key header, with no container to run and no Google account anywhere in the flow. A client that only speaks stdio reaches it through a thin launcher, published as @hasdata/google-trends-mcp on npm and hasdata-google-trends-mcp on PyPI, shown below.

Quick start

The server URL is the same for every client. We run it hands-on in Claude Code and Claude Desktop. The other blocks follow each client's own documented format for a remote server.

Clients with OAuth support can add the same URL as a connector and sign in without putting a key in a config file.

Claude Code

claude mcp add --transport http google-trends "https://mcp.hasdata.com/mcp?apis=google_trends" \

--header "x-api-key: HASDATA_API_KEY"

Claude Desktop

Settings, then Connectors, then Add custom connector, then paste https://mcp.hasdata.com/mcp?apis=google_trends and sign in.

For the config-file route, Claude Desktop loads only local (stdio) servers, so it reaches a remote server through a stdio launcher. The @hasdata/google-trends-mcp package is that launcher, and it reads the key from the environment. Add this to claude_desktop_config.json:

{

"mcpServers": { "google-trends": { "command": "npx", "args": ["-y", "@hasdata/google-trends-mcp"], "env": { "HASDATA_API_KEY": "YOUR_KEY" } } } }

For Python instead of Node, swap the launcher for the PyPI package, which uvx runs without a manual install:

{

"mcpServers": { "google-trends": { "command": "uvx", "args": ["hasdata-google-trends-mcp"], "env": { "HASDATA_API_KEY": "YOUR_KEY" } } } }

Cursor

~/.cursor/mcp.json for every project, or .cursor/mcp.json for one:

{

"mcpServers": { "google-trends": { "url": "https://mcp.hasdata.com/mcp?apis=google_trends", "headers": { "x-api-key": "HASDATA_API_KEY" } } } }

Windsurf

~/.codeium/windsurf/mcp_config.json. Windsurf calls the field serverUrl, not url:

{

"mcpServers": { "google-trends": { "serverUrl": "https://mcp.hasdata.com/mcp?apis=google_trends", "headers": { "x-api-key": "HASDATA_API_KEY" } } } }

VS Code

.vscode/mcp.json in the workspace:

{

"servers": { "google-trends": { "type": "http", "url": "https://mcp.hasdata.com/mcp?apis=google_trends", "headers": { "x-api-key": "HASDATA_API_KEY" } } } }

Example prompts

Prompts, not code. Paste one in and the agent picks the tool itself. Each is annotated with the calls it takes, because every successful call costs 10 credits.

Chart interest in "cold brew coffee" in the US over the past 12 months and tell me which weeks it peaked.

*One call, 10 credits. The weekly series comes back in a single request.*

For "cold brew coffee" in the US, give me the rising related queries and flag the ones marked Breakout.

*One call, 10 credits.*

Compare interest in "cold brew" against "iced coffee" worldwide over five years and say which one is growing.

*One call, 10 credits. The tool takes several terms in one timeseries request.*

Show me interest in "sunscreen" by US state over the past 90 days so I can see where demand is highest.

*One call, 10 credits. This is the interest-by-region view at state granularity.*

A comparison across terms rides in one timeseries call. Region breakdowns, related queries and related topics are each their own dataType, so a prompt that wants a chart plus its rising queries is two calls.

Tools

One tool, read-only. The sample below is trimmed from a real call, and the numbers move as the trend moves. Read it as a shape. The tool name links to its endpoint reference, which carries the full parameter list.

The sample is the payload, not the whole response. A tools/call result carries one text block, and that text is itself JSON holding url, status, text and json, with the scraped data under json. From a raw JSON-RPC response the path is result.content[0].text, parsed, then .json. A chat client unwraps that for you and code talking to the endpoint directly does not.

Get Google Trends data

hasdata_google_trends_search_getTrendsData

Interest over time, by region, or the related queries and topics for a term.

The response key depends on dataType. timeseries returns interestOverTime.timelineData, geoMap returns interest by region, and the related types return relatedQueries or relatedTopics, each split into rising and top. Read the key that matches the type you asked for.

timeseries (the default) returns a value from 0 to 100 for each point, both as a string and pre-parsed in extractedValue. The most recent point often carries isPartial: true, meaning the week is still filling in. Drop it before you compute a trend, or the last bar reads as a dip that is not real.

{

"interestOverTime": { "timelineData": [ { "date": "Apr 12 – 18, 2026", "timestamp": "1775952000", "isPartial": false, "values": [{ "query": "cold brew coffee", "value": "100", "extractedValue": 100, "hasData": true }] }, { "date": "Aug 23 – 29, 2026", "timestamp": "1787443200", "isPartial": true, "values": [{ "query": "cold brew coffee", "value": "44", "extractedValue": 44, "hasData": true }] } ] } }

relatedQueries splits into rising and top. A rising entry reads as a percentage like +300%, or Breakout for a jump too large to score, and extractedValue gives the number behind it. A Breakout comes back with a sentinel extractedValue well above any real percentage, so sort on the string label, not on the raw number.

{

"relatedQueries": { "rising": [ { "query": "organic cold brew coffee", "value": "+300%", "extractedValue": 300, "link": "https://trends.google.com/trends/explore?q=organic+cold+brew+coffee&date=today+12-m&geo=US" } ], "top": [ { "query": "how to cold brew coffee", "value": "100", "extractedValue": 100, "link": "https://trends.google.com/trends/explore?q=how+to+cold+brew+coffee&date=today+12-m&geo=US" } ] } }

The endpoint reference lists every geo, cat and date format the tool accepts.

Prompts and resources

The server ships one prompt, a ready-made workflow a client can offer instead of making the user compose a tool call.

Alongside them the server exposes 5 resources, one per parameter whose accepted values are a fixed list. Reading one is cheaper than learning the vocabulary from a rejected call, and it costs no credits. Each URI is hasdata://google_trends/ .

| dataType | 4 | Defines the type of search to perform. Available options: - timeseries: Interest over time (default). Accepts both single and multiple queries per search. - geoMap: Interest by region. Accepts both single and mul