V2.ai Insights Scraper MCP
A Model Context Protocol (MCP) server that scrapes blog posts from V2.ai Insights, extracts content, and provides AI-powered summaries using OpenAI's GPT-4. Currently supports Contentful CMS integration with search capabilities.
📋 Strategic Vision: This project is evolving into a comprehensive AI intelligence platform. See STRATEGIC_VISION.md for the complete roadmap from content API to strategic intelligence platform.
Features
- 🔍 Multi-Source Content: Fetches from Contentful CMS and V2.ai web scraping
- 📝 Content Extraction: Extracts title, date, author, and content with intelligent fallbacks
- 🔎 Full-Text Search: Search across all blog content with Contentful's search API
- 🤖 AI Summarization: Generates summaries using OpenAI GPT-4
- 🔧 MCP Integration: Exposes tools for Claude Desktop integration
Tools Available
get_latest_posts()- Retrieves blog posts with metadata (Contentful + V2.ai fallback)get_contentful_posts(limit)- Fetch posts directly from Contentful CMSsearch_blogs(query, limit)- NEW - Search across all blog contentsummarize_post(index)- Returns AI-generated summary of a specific postget_post_content(index)- Returns full content of a specific post
Setup
Prerequisites
- Python 3.12+
- uv package manager
- OpenAI API key
- Contentful CMS credentials (optional, for enhanced functionality)
Installation
-
Clone and navigate to project:
cd v2-ai-mcp -
Install dependencies:
uv add fastmcp beautifulsoup4 requests openai -
Set up environment variables:
Create a
.envfile based on.env.example:cp .env.example .envEdit
.envwith your credentials:# Required OPENAI_API_KEY=your-openai-api-key-here # Optional (for Contentful integration) CONTENTFUL_SPACE_ID=your-contentful-space-id CONTENTFUL_ACCESS_TOKEN=your-contentful-access-token CONTENTFUL_CONTENT_TYPE=pageBlogPost
Running the Server
uv run python -m src.v2_ai_mcp.main
The server will start and be available for MCP connections.
Testing the Scraper
Test individual components:
# Test scraper
uv run python -c "from src.v2_ai_mcp.scraper import fetch_blog_posts; print(fetch_blog_posts()[0]['title'])"
# Test with summarizer (requires OpenAI API key)
uv run python -c "from src.v2_ai_mcp.scraper import fetch_blog_posts; from src.v2_ai_mcp.summarizer import summarize; post = fetch_blog_posts()[0]; print(summarize(post['content'][:1000]))"
# Run unit tests
uv run pytest tests/ -v --cov=src
Claude Desktop Integration
Configuration
-
Install Claude Desktop (if not already installed)
-
Configure MCP in Claude Desktop:
Add to your Claude Desktop MCP configuration:
{ "mcpServers": { "v2-insights-scraper": { "command": "/path/to/uv", "args": ["run", "--directory", "/path/to/your/v2-ai-mcp", "python", "-m", "src.v2_ai_mcp.main"], "env": { "OPENAI_API_KEY": "your-api-key-here", "CONTENTFUL_SPACE_ID": "your-contentful-space-id", "CONTENTFUL_ACCESS_TOKEN": "your-contentful-access-token", "CONTENTFUL_CONTENT_TYPE": "pageBlogPost" } } } } -
Restart Claude Desktop to load the MCP server
Using the Tools
Once configured, you can use these tools in Claude Desktop:
- Get latest posts:
get_latest_posts()(intelligent Contentful + V2.ai fallback) - Get Contentful posts:
get_contentful_posts(10)(direct CMS access) - Search blogs:
search_blogs("AI automation", 5)(NEW - full-text search) - Summarize post:
summarize_post(0)(index 0 for first post) - Get full content:
get_post_content(0)
Example Usage
🔍 Search for AI-related content:
search_blogs("artificial intelligence", 3)
📚 Get latest posts with automatic source selection:
get_latest_posts()
🤖 Get AI summary of specific post:
summarize_post(0)
Project Structure
v2-ai-mcp/
├── src/
│ └── v2_ai_mcp/
│ ├── __init__.py # Package initialization
│ ├── main.py # FastMCP server with tool definitions
│ ├── scraper.py # Web scraping logic
│ └── summarizer.py # OpenAI GPT-4 integration
├── tests/
│ ├── __init__.py # Test package initialization
│ ├── test_scraper.py # Unit tests for scraper
│ └── test_summarizer.py # Unit tests for summarizer
├── .github/
│ └── workflows/
│ └── ci.yml # GitHub Actions CI/CD pipeline
├── pyproject.toml # Project dependencies and config
├── .env.example # Environment variables template
├── .gitignore # Git ignore patterns
└── README.md # This file
Current Implementation
The scraper currently targets this specific blog post:
- URL:
https://www.v2.ai/insights/adopting-AI-assistants-while-balancing-risks
Extracted Data
- Title: "Adopting AI Assistants while Balancing Risks"
- Author: "Ashley Rodan"
- Date: "July 3, 2025"
- Content: ~12,785 characters of main content
Development
Adding More Blog Posts
To scrape multiple posts or different URLs, modify the fetch_blog_posts() function in scraper.py:
def fetch_blog_posts() -> list:
urls = [
"https://www.v2.ai/insights/post1",
"https://www.v2.ai/insights/post2",
# Add more URLs
]
return [fetch_blog_post(url) for url in urls]
Improving Content Extraction
The scraper uses multiple fallback strategies for extracting content. You can enhance it by:
- Inspecting V2.ai's HTML structure
- Adding more specific CSS selectors
- Improving date/author extraction patterns
Troubleshooting
Common Issues
- OpenAI API Key Error: Ensure your API key is set in environment variables
- Import Errors: Run
uv syncto ensure all dependencies are installed - Scraping Issues: Check if the target URL is accessible and the HTML structure hasn't changed
Testing Components
# Test scraper only
uv run python -c "from src.v2_ai_mcp.scraper import fetch_blog_posts; posts = fetch_blog_posts(); print(f'Found {len(posts)} posts')"
# Run full test suite
uv run pytest tests/ -v --cov=src
# Test MCP server startup
uv run python -m src.v2_ai_mcp.main
Development
Running Tests
# Run all tests
uv run pytest
# Run with coverage
uv run pytest --cov=src --cov-report=html
# Run specific test file
uv run pytest tests/test_scraper.py -v
Code Quality
# Format code
uv run ruff format src tests
# Lint code
uv run ruff check src tests
# Fix auto-fixable issues
uv run ruff check --fix src tests
License
This project is for educational and development purposes.