# Google Trends MCP — AI Agent Trend Intelligence (`red.cars/google-trends-mcp`) Actor Track Google Trends data for AI agents. Daily trending searches, topic interest over time, related queries. Built for content marketers, news bots, and social media schedulers. - **URL**: https://apify.com/red.cars/google-trends-mcp.md - **Developed by:** [AutomateLab](https://apify.com/red.cars) (community) - **Categories:** AI, Automation, Developer tools - **Stats:** 1 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event + usage This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Google Trends MCP **AI-native Google Trends API for autonomous workflows.** Programmatic access to daily trending searches, interest over time, related queries, topic breakdowns, and category filtering. Built for AI agents, content marketers, SEO tools, and market intelligence workflows. --- ### Hero ```` ┌─────────────────────────────────────────────────────────────────┐ │ GOOGLE TRENDS MCP │ │ │ │ Daily Trends │ Interest Over Time │ Related Queries │ │ │ │ Categories │ Topic Breakdown │ Multi-Geo │ └─────────────────────────────────────────────────────────────────┘ ```` ### Quick Start ```json { "tool": "get_daily_trending", "arguments": { "location": "US", "category": "0" } } ```` *** ### Tools #### 1. get\_daily\_trending Get what's trending right now — top daily search queries that are spiking. **When to call:** You need real-time trending topics for content planning, news alerts, or social media scheduling. **Example AI prompt:** *"What's trending in technology today?"* **Input:** ```json { "location": "string", // Required: US, GB, DE, FR, etc. "category": "string" // Optional: 0=all, 1=Art, 2=Science, etc. } ``` **Output:** ```json { "trending": [ { "query": "Apple keynote", "traffic": 100000, "delta": "+1200%" }, { "query": "SpaceX launch", "traffic": 85000, "delta": "+850%" } ], "location": "US", "category": "all", "timestamp": "2026-05-17T12:00:00Z" } ``` **PPE:** $0.03 *** #### 2. get\_topic\_trends Get interest over time for a specific topic — historical trend lines with daily/weekly/monthly granularity. **When to call:** You need to analyze how a topic's popularity has changed over time, identify seasonality, or compare multiple topics. **Example AI prompt:** *"Show me the interest over time for electric vehicles over the past 3 years."* **Input:** ```json { "topic": "string", // Required: Topic name "time_range": "string" // Optional: today 1-m, today 3-m, today 12-m, today 5-y } ``` **Output:** ```json { "topic": "electric vehicles", "interest_over_time": [ { "date": "2026-02", "score": 45 }, { "date": "2026-03", "score": 52 }, { "date": "2026-04", "score": 61 } ], "peak": { "date": "2026-04", "score": 68 }, "average": 54, "time_range": "today 3-m" } ``` **PPE:** $0.05 *** #### 3. get\_related\_queries Get related queries for a seed topic — what people also search for alongside your query. **When to call:** You need keyword research, content ideas, or to understand search intent beyond your initial query. **Example AI prompt:** *"What queries are related to 'machine learning'?"* **Input:** ```json { "seed_query": "string", // Required: Seed search query "geo": "string", // Optional: US, GB, DE, etc. "category": "string" // Optional: 0-99 } ``` **Output:** ```json { "seed_query": "machine learning", "top_queries": [ { "query": "machine learning course", "value": 100, "category": "rising" }, { "query": "machine learning algorithms", "value": 85, "category": "top" } ], "rising_queries": [ { "query": "machine learning for beginners", "value": "+500%" } ], "geo": "US" } ``` **PPE:** $0.03 *** #### 4. get\_category\_browse Browse Google Trends categories to discover available topics and subtopics. **When to call:** You want to explore what categories are available for trend analysis. **Example AI prompt:** *"What categories are available in Google Trends?"* **Input:** ```json { "category": "string" // Optional: Category ID, defaults to all } ``` **Output:** ```json { "categories": [ { "id": 0, "name": "All categories", "subtopics": [] }, { "id": 1, "name": "Art", "subtopics": ["Photography", "Art History"] }, { "id": 2, "name": "Science", "subtopics": ["Physics", "Chemistry"] } ] } ``` **PPE:** $0.03 *** #### 5. search\_trends Combined search that returns interest over time, top queries, and rising queries in one call. **When to call:** You need a complete trend analysis for a query — multi-signal in a single call. **Example AI prompt:** *"Give me a complete trend analysis for 'AI assistants'."* **Input:** ```json { "query": "string", // Required: Search query "geo": "string", // Optional: US, GB, DE, etc. "time_range": "string" // Optional: today 1-m, today 3-m, today 12-m } ``` **Output:** ```json { "query": "AI assistants", "interest_over_time": [...], "top_queries": [...], "rising_queries": [...], "geo": "US", "time_range": "today 3-m", "timestamp": "2026-05-17T12:00:00Z" } ``` **PPE:** $0.05 *** ### Data Source Google Trends public API — no API key required. *** ### How It Compares **Feature** | **Google Trends MCP** | **Similarweb** | **Manual Google Trends** \---|---|---|--- Price | $0.03–$0.05/call | $100+/mo | Free but manual AI-native (MCP) | ✅ Yes | ❌ No | ❌ No Programmatic | ✅ Yes | ✅ Yes | ❌ No Historical data | ✅ 1+ years | ✅ Yes | Limited Related queries | ✅ Yes | ✅ Yes | ✅ Manual Real-time trends | ✅ Yes | ✅ Yes | ✅ Manual **Why choose our MCP:** - MCP protocol is designed for AI agent integration — incorporate trend data into autonomous workflows - $0.03–$0.05 per call vs $100+/month subscriptions — 50x cheaper for high-volume AI use cases - Batch-friendly: chain multiple trend queries for competitive analysis pipelines - No manual work — fully automated trend detection and reporting *** ### Pricing **Action** | **PPE Cost** \---|--- get\_daily\_trending | $0.03 get\_topic\_trends | $0.05 get\_related\_queries | $0.03 get\_category\_browse | $0.03 search\_trends | $0.05 *** ### Architecture ``` ┌─────────────────────────────────────────────────────────────────┐ │ google-trends-mcp │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ Input: { query, geo?, category?, time_range? } │ │ │ │ ┌─────────────────────────────────────────────────────────┐ │ │ │ Google Trends Data Sources │ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ │ │ Trending │ │ Interest │ │ Related │ │ │ │ │ │ Daily │ │ Time │ │ Queries │ │ │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ │ └─────────────────────────────────────────────────────────┘ │ │ │ │ Output: { trending[], interest_over_time[], related[] } │ └─────────────────────────────────────────────────────────────────┘ ``` *** ### Cross-Sells #### content-marketing-mcp (coming soon) For content marketing workflows — trending topics fuel content generation. *** ### MCP Protocol This actor implements the MCP (Model Context Protocol) for AI agent integration. **Endpoint:** `/mcp` **Request format:** ```json { "tool": "get_daily_trending", "arguments": { "location": "US", "category": "0" } } ``` **Response format:** ```json { "success": true, "result": { ... } } ``` *** ### Deployment This actor runs in **standby mode** on Apify, enabling efficient AI agent integration with pay-per-event pricing. **Actor ID:** `google-trends-mcp` **Pricing:** Event-based (PPE) — $0.03–$0.05 per call *** ### Status - **Created:** 2026-05-17 - **Data source:** Google Trends public API (no auth required) - **Tools:** 5 (get\_daily\_trending, get\_topic\_trends, get\_related\_queries, get\_category\_browse, search\_trends) - **PPE range:** $0.03–$0.05 *** # Actor input Schema ## `query` (type: `string`): Search query or topic to get trends for ## `geo` (type: `string`): Geographic location (US, GB, DE, etc.) ## `category` (type: `string`): Category filter (0-99, 0=all) ## `time_range` (type: `string`): Time range: now 1h, now 4h, now 1d, now 7d, today 1-m, today 3-m, today 12-m ## Actor input object example ```json { "query": "", "geo": "US", "category": "0", "time_range": "today 3-m" } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "query": "", "geo": "US", "category": "0", "time_range": "today 3-m" }; // Run the Actor and wait for it to finish const run = await client.actor("red.cars/google-trends-mcp").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "query": "", "geo": "US", "category": "0", "time_range": "today 3-m", } # Run the Actor and wait for it to finish run = client.actor("red.cars/google-trends-mcp").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "query": "", "geo": "US", "category": "0", "time_range": "today 3-m" }' | apify call red.cars/google-trends-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=red.cars/google-trends-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Trends MCP — AI Agent Trend Intelligence", "description": "Track Google Trends data for AI agents. Daily trending searches, topic interest over time, related queries. Built for content marketers, news bots, and social media schedulers.", "version": "1.0", "x-build-id": "Fw0ePzXxOkmsIBguh" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/red.cars~google-trends-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-red.cars-google-trends-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/red.cars~google-trends-mcp/runs": { "post": { "operationId": "runs-sync-red.cars-google-trends-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/red.cars~google-trends-mcp/run-sync": { "post": { "operationId": "run-sync-red.cars-google-trends-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "query" ], "properties": { "query": { "title": "Query", "type": "string", "description": "Search query or topic to get trends for", "default": "" }, "geo": { "title": "Geo", "type": "string", "description": "Geographic location (US, GB, DE, etc.)", "default": "US" }, "category": { "title": "Category", "type": "string", "description": "Category filter (0-99, 0=all)", "default": "0" }, "time_range": { "title": "Time Range", "type": "string", "description": "Time range: now 1h, now 4h, now 1d, now 7d, today 1-m, today 3-m, today 12-m", "default": "today 3-m" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```