apify-mcp-server
🥈SilverThe Apify MCP server enables your AI agents to extract data from social media, search engines, maps, e-commerce sites, or any other website using thousands of ready-made scrapers, crawlers, and automation tools available on the Apify Store.
1,029970Updated 3d ago
Intermediate30min to implementmarketing
Saves ~135 min per use
Quick InstallView Source
git clone https://github.com/apify/apify-mcp-server.gitWorks with:
Claude
Overview
About This Skill
https://docs.apify.com/platform/integrations/mcp
How to Use
[{"step":"Install the Apify MCP server and ensure you have an Apify API token. You can obtain this from your Apify account settings under 'Integrations'.","tip":"Use the Apify CLI or integrate the MCP server directly into your AI agent workflow. Ensure your API token has the necessary permissions for the scrapers you plan to use."},{"step":"Identify the target website and data you need to extract. Use the Apify Store to find a pre-built scraper that matches your requirements. For example, search for 'Amazon Product Reviews Scraper' or 'Google Maps Reviews Scraper'.","tip":"Check the scraper's documentation on Apify to confirm it supports the specific data fields you need (e.g., ratings, review text, dates)."},{"step":"Configure the scraper with your inputs, such as the target URL, search queries, or filters. For instance, if scraping Amazon reviews, you might specify a product URL and a date range to focus on recent feedback.","tip":"Use the 'Run' tab in the Apify console to test the scraper with a small dataset before scaling up. This helps avoid hitting rate limits or collecting irrelevant data."},{"step":"Run the scraper and export the results in your desired format (CSV, JSON, or Excel). If using the Apify MCP server, you can directly pipe the output into your AI agent for further analysis.","tip":"For large datasets, consider using Apify's 'Dataset' storage to manage and filter results before exporting. This can save time and reduce processing overhead."},{"step":"Analyze the scraped data within your AI agent or export it to a tool like Google Sheets, Excel, or a BI platform (e.g., Tableau, Power BI) for visualization and deeper insights.","tip":"Use the AI agent to summarize key trends, identify outliers, or generate actionable recommendations based on the scraped data. For example, highlight common complaints or praise to inform product improvements."}]
Use Cases
Automate the extraction of product prices and reviews from e-commerce websites for market analysis.
Gather social media engagement metrics from multiple platforms to evaluate marketing campaign performance.
Extract contact details from Google Maps for lead generation in sales and marketing.
Scrape Google Search Engine Results Pages (SERPs) to analyze SEO performance and keyword rankings.
Tags
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Alternative Install (Git Clone)
git clone https://github.com/apify/apify-mcp-serverRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Use the Apify MCP server to scrape [WEBSITE_URL] and extract [SPECIFIC_DATA_TYPE] such as product listings, customer reviews, or contact details. Focus on [KEY_ATTRIBUTES] like pricing, ratings, or availability. Export the results in [DESIRED_FORMAT] (CSV, JSON, or Excel) and include a summary of key insights or trends. Example: 'Use the Apify MCP server to scrape https://www.amazon.com/product-reviews/B08N5KWB9H and extract customer reviews, ratings, and review dates. Focus on reviews with 1-3 stars and highlight common complaints about durability or battery life. Export the results as a CSV and include a summary of the top 3 recurring issues.'
Example Output
### Scraped Data Summary: Amazon Product Reviews for 'Wireless Bluetooth Earbuds X1' **Source URL:** https://www.amazon.com/product-reviews/B08N5KWB9H **Scraping Tool Used:** Apify MCP server with the 'Amazon Product Reviews Scraper' **Total Reviews Extracted:** 1,247 **Date Range:** Last 12 months #### Key Metrics: - **Average Rating:** 3.2/5 stars - **Total 1-3 Star Reviews:** 412 (33% of total) - **Most Common Complaints:** 1. **Poor Battery Life (189 mentions):** Users report the earbuds last only 3-4 hours on a single charge, far below the advertised 8 hours. 2. **Fit Issues (124 mentions):** Many customers note the earbuds fall out during workouts or active use. 3. **Sound Quality (98 mentions):** Complaints about muffled audio or lack of bass in lower-priced models. #### Sample Reviews (1-3 stars): 1. **"Battery dies in 3 hours"** (⭐⭐) - *Reviewed on 2023-11-15* "Advertised as 8 hours, but real-world usage is closer to 3. Charging case is bulky and slow to recharge." 2. **"Earbuds keep falling out"** (⭐) - *Reviewed on 2023-12-03* "Went for a run and had to stop twice to put them back in. Not sweat-resistant like the description claims." 3. **"Sound is tinny"** (⭐⭐) - *Reviewed on 2024-01-20* "For the price, I expected better bass. Highs are sharp, but mids and lows are weak." #### Export Details: - **File Format:** CSV - **Columns Included:** Review Date, Rating, Review Title, Review Text, Verified Purchase, Helpful Votes - **File Size:** 1.2 MB (1,247 rows) #### Recommendations: - **For Competitor Analysis:** Highlight the battery life issue in marketing materials for your own wireless earbuds. Consider offering a 24-month warranty to build trust. - **For Product Improvement:** Invest in battery optimization or offer a swap program for customers who report issues. - **For Customer Support:** Proactively reach out to customers who left 1-2 star reviews to offer replacements or refunds as a goodwill gesture. **Next Steps:** 1. Use this data to inform your product roadmap for the next generation of earbuds. 2. Share the CSV file with your customer support team to identify customers who may need follow-up. 3. Compare these findings with reviews of your own products to identify gaps in your offerings.
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