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

Related MCP server: MCP-Twikit

Tools Available

• get_latest_posts() - Retrieves blog posts with metadata (Contentful + V2.ai fallback)

• get_contentful_posts(limit) - Fetch posts directly from Contentful CMS

• search_blogs(query, limit) - NEW - Search across all blog content

• summarize_post(index) - Returns AI-generated summary of a specific post

• get_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

1. Clone and navigate to project:

   cd v2-ai-mcp

2. Install dependencies:

   uv add fastmcp beautifulsoup4 requests openai

3. Set up environment variables:

   Create a .env file based on .env.example:

   cp .env.example .env

   Edit .env with 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

1. Install Claude Desktop (if not already installed)

2. 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"
         }
       }
     }
   }

3. 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:

1. Inspecting V2.ai's HTML structure

2. Adding more specific CSS selectors

3. Improving date/author extraction patterns

Troubleshooting

Common Issues

1. OpenAI API Key Error: Ensure your API key is set in environment variables

2. Import Errors: Run uv sync to ensure all dependencies are installed

3. 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.