"""
Article Quadrant Analyzer MCP Server
An MCP server that extracts content from articles, performs insightful analysis,
and generates visual 2x2 quadrant representations using SVG graphics.
"""
import asyncio
import logging
import os
import sys
from pathlib import Path
from typing import Any, Dict, Optional
import fastmcp
from fastmcp import FastMCP, Context
# Add the src directory to the path for imports
sys.path.insert(0, str(Path(__file__).parent.parent))
from mcp_server_article_quadrant.tools.extract_content import extract_article_content
from mcp_server_article_quadrant.tools.analyze_insights import analyze_article_insights
# Configure logging
logging.basicConfig(
level=getattr(logging, os.getenv("LOG_LEVEL", "INFO")),
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)
# Create the MCP server
app = FastMCP("article-quadrant-analyzer")
@app.tool()
async def extract_article_content(
source: Dict[str, Any],
options: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
"""
Extract clean article content from URLs, local files, or direct text input with automatic preprocessing and metadata extraction.
Args:
source: Content source specification with type (url, file_path, direct_text) and content
options: Extraction options including metadata extraction, HTML cleaning, timeouts
Returns:
Extracted content with title, text, metadata, and processing information
"""
logger.info(f"Extracting content from source type: {source.get('type')}")
return await extract_article_content(source, options)
@app.tool()
async def analyze_article_insights(
content: Dict[str, Any],
analysis_options: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
"""
Extract key insights, topics, and classifications from article content using natural language processing.
Args:
content: Article content with title, text, and metadata
analysis_options: Analysis configuration including topic extraction, sentiment analysis, language settings
Returns:
Structured insights with topics, key points, entities, and sentiment analysis
"""
logger.info("Analyzing article insights")
return await analyze_article_insights(content, analysis_options)
@app.tool()
async def generate_quadrant_analysis(
insights: Dict[str, Any],
quadrant_config: Dict[str, Any],
visualization_options: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
"""
Generate comprehensive 2x2 quadrant analysis with professional SVG visualization based on article insights.
Args:
insights: Analysis results from analyze_article_insights
quadrant_config: Quadrant configuration with axes and labels
visualization_options: Visual styling options including colors, fonts, layout
Returns:
Complete quadrant analysis with SVG visualization and metadata
"""
logger.info("Generating quadrant analysis")
# This will be implemented in the tools/generate_quadrant.py module
# For now, return a placeholder response
return {
"success": False,
"error": {
"code": "NOT_IMPLEMENTED",
"message": "Tool implementation in progress"
},
"processing_time": 0.0
}
@app.tool()
async def create_custom_quadrant(
title: str,
x_axis: Dict[str, Any],
y_axis: Dict[str, Any],
insights: list,
styling: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
"""
Create custom quadrant analysis with user-defined parameters and manual insight placement.
Args:
title: Analysis title
x_axis: X-axis configuration with label and dimension settings
y_axis: Y-axis configuration with label and dimension settings
insights: List of insights with content and positioning information
styling: Optional visual styling configuration
Returns:
Custom quadrant analysis with SVG visualization and positioning data
"""
logger.info(f"Creating custom quadrant: {title}")
# This will be implemented in the tools/create_custom_quadrant.py module
# For now, return a placeholder response
return {
"success": False,
"error": {
"code": "NOT_IMPLEMENTED",
"message": "Tool implementation in progress"
},
"processing_time": 0.0
}
def main() -> None:
"""Main entry point for the MCP server."""
logger.info("Starting Article Quadrant Analyzer MCP Server")
# Get configuration from environment variables
cache_dir = os.getenv("ANALYSIS_CACHE_DIR", "~/.cache/article-analyzer")
max_content_length = int(os.getenv("MAX_CONTENT_LENGTH", "50000"))
log_level = os.getenv("LOG_LEVEL", "INFO")
logger.info(f"Configuration: cache_dir={cache_dir}, max_content_length={max_content_length}, log_level={log_level}")
# Run the server
app.run()
if __name__ == "__main__":
main()
