feat: Implement PyPI Discovery & Monitoring Tools
Add comprehensive PyPI discovery and monitoring capabilities with 4 core tools: - monitor_pypi_new_releases: Track new releases by category with real-time alerts - get_pypi_trending_today: Analyze current trending packages with market insights - search_pypi_by_maintainer: Find packages by maintainer with portfolio analysis - get_pypi_package_recommendations: Algorithm-based package recommendations Features: - RSS feed integration for real-time release monitoring - Intelligent caching system with configurable TTL - Advanced package categorization and filtering - Trending analysis with multiple data signals - Personalized recommendations with user context - Comprehensive error handling and logging - Full test coverage with mocked dependencies - MCP server endpoints for all discovery tools Dependencies: - Add feedparser for RSS feed parsing - Enhanced server.py with 4 new MCP tool endpoints - Updated tools/__init__.py exports This implementation provides production-ready monitoring and discovery capabilities that integrate seamlessly with the existing codebase architecture.
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@ -53,6 +53,12 @@ from .tools import (
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update_package_metadata,
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upload_package_to_pypi,
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)
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from .tools.discovery import (
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get_pypi_package_recommendations,
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get_pypi_trending_today,
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monitor_pypi_new_releases,
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search_pypi_by_maintainer,
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)
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# Configure logging
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logging.basicConfig(
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@ -1888,6 +1894,233 @@ async def analyze_package_competition(
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}
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# PyPI Discovery & Monitoring Tools
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@mcp.tool()
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async def monitor_pypi_new_releases_tool(
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categories: list[str] | None = None,
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hours: int = 24,
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min_downloads: int | None = None,
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maintainer_filter: str | None = None,
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enable_notifications: bool = False,
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cache_ttl: int = 300,
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) -> dict[str, Any]:
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"""Track new releases in specified categories over a time period.
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This tool monitors PyPI for new package releases, providing comprehensive tracking
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and analysis of recent activity in the Python ecosystem.
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Args:
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categories: List of categories to monitor (e.g., ["web", "data-science", "ai", "cli"])
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hours: Number of hours to look back for new releases (default: 24)
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min_downloads: Minimum monthly downloads to include (filters out very new packages)
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maintainer_filter: Filter releases by specific maintainer names
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enable_notifications: Whether to enable alert system for monitoring
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cache_ttl: Cache time-to-live in seconds (default: 300)
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Returns:
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Dictionary containing new releases with metadata, analysis, and alerts
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Raises:
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NetworkError: If unable to fetch release data
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SearchError: If category filtering fails
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"""
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try:
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logger.info(f"MCP tool: Monitoring new PyPI releases for {hours}h, categories: {categories}")
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result = await monitor_pypi_new_releases(
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categories=categories,
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hours=hours,
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min_downloads=min_downloads,
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maintainer_filter=maintainer_filter,
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enable_notifications=enable_notifications,
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cache_ttl=cache_ttl,
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)
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logger.info(f"Successfully monitored releases: {result['total_releases_found']} found")
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return result
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except (NetworkError, SearchError) as e:
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logger.error(f"Error monitoring new releases: {e}")
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return {
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"error": str(e),
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"error_type": type(e).__name__,
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"categories": categories,
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"hours": hours,
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}
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except Exception as e:
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logger.error(f"Unexpected error monitoring new releases: {e}")
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return {
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"error": f"Unexpected error: {e}",
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"error_type": "UnexpectedError",
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"categories": categories,
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"hours": hours,
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}
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@mcp.tool()
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async def get_pypi_trending_today_tool(
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category: str | None = None,
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min_downloads: int = 1000,
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limit: int = 50,
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include_new_packages: bool = True,
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trending_threshold: float = 1.5,
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) -> dict[str, Any]:
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"""Get packages that are trending on PyPI right now based on recent activity.
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This tool analyzes current PyPI trends to identify packages gaining popularity
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or showing significant activity increases today.
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Args:
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category: Optional category filter ("web", "ai", "data-science", etc.)
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min_downloads: Minimum daily downloads to be considered trending
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limit: Maximum number of trending packages to return
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include_new_packages: Include recently released packages in trending analysis
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trending_threshold: Multiplier for determining trending status (1.5 = 50% increase)
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Returns:
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Dictionary containing trending packages with activity metrics and market insights
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Raises:
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SearchError: If trending analysis fails
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NetworkError: If unable to fetch trending data
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"""
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try:
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logger.info(f"MCP tool: Analyzing today's PyPI trends, category: {category}")
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result = await get_pypi_trending_today(
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category=category,
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min_downloads=min_downloads,
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limit=limit,
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include_new_packages=include_new_packages,
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trending_threshold=trending_threshold,
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)
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logger.info(f"Successfully analyzed trends: {result['total_trending']} packages found")
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return result
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except (SearchError, NetworkError) as e:
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logger.error(f"Error analyzing trending packages: {e}")
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return {
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"error": str(e),
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"error_type": type(e).__name__,
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"category": category,
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"limit": limit,
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}
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except Exception as e:
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logger.error(f"Unexpected error analyzing trends: {e}")
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return {
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"error": f"Unexpected error: {e}",
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"error_type": "UnexpectedError",
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"category": category,
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"limit": limit,
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}
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@mcp.tool()
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async def search_pypi_by_maintainer_tool(
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maintainer: str,
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include_email: bool = False,
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sort_by: str = "popularity",
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limit: int = 50,
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include_stats: bool = True,
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) -> dict[str, Any]:
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"""Find all packages maintained by a specific maintainer or organization.
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This tool searches PyPI to find all packages associated with a particular
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maintainer, providing comprehensive portfolio analysis.
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Args:
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maintainer: Maintainer name or email to search for
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include_email: Whether to search by email addresses too
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sort_by: Sort results by ("popularity", "recent", "name", "downloads")
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limit: Maximum number of packages to return
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include_stats: Include download and popularity statistics
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Returns:
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Dictionary containing packages by the maintainer with detailed portfolio analysis
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Raises:
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InvalidPackageNameError: If maintainer name is invalid
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SearchError: If maintainer search fails
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"""
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try:
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logger.info(f"MCP tool: Searching packages by maintainer: '{maintainer}'")
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result = await search_pypi_by_maintainer(
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maintainer=maintainer,
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include_email=include_email,
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sort_by=sort_by,
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limit=limit,
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include_stats=include_stats,
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)
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logger.info(f"Successfully found {result['total_packages']} packages for maintainer")
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return result
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except (InvalidPackageNameError, SearchError) as e:
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logger.error(f"Error searching by maintainer {maintainer}: {e}")
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return {
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"error": str(e),
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"error_type": type(e).__name__,
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"maintainer": maintainer,
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}
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except Exception as e:
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logger.error(f"Unexpected error searching by maintainer {maintainer}: {e}")
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return {
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"error": f"Unexpected error: {e}",
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"error_type": "UnexpectedError",
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"maintainer": maintainer,
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}
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@mcp.tool()
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async def get_pypi_package_recommendations_tool(
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package_name: str,
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recommendation_type: str = "similar",
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limit: int = 20,
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include_alternatives: bool = True,
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user_context: dict[str, Any] | None = None,
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) -> dict[str, Any]:
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"""Get PyPI's algorithm-based package recommendations and suggestions.
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This tool provides intelligent package recommendations using advanced algorithms
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that consider functionality, popularity, and user context.
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Args:
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package_name: Base package to get recommendations for
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recommendation_type: Type of recommendations ("similar", "complementary", "upgrades", "alternatives")
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limit: Maximum number of recommendations to return
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include_alternatives: Include alternative packages that serve similar purposes
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user_context: Optional user context for personalized recommendations (use_case, experience_level, etc.)
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Returns:
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Dictionary containing personalized package recommendations with detailed analysis
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Raises:
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PackageNotFoundError: If base package is not found
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SearchError: If recommendation generation fails
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"""
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try:
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logger.info(f"MCP tool: Generating recommendations for package: '{package_name}'")
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result = await get_pypi_package_recommendations(
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package_name=package_name,
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recommendation_type=recommendation_type,
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limit=limit,
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include_alternatives=include_alternatives,
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user_context=user_context,
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)
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logger.info(f"Successfully generated {result['total_recommendations']} recommendations")
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return result
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except (PackageNotFoundError, SearchError) as e:
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logger.error(f"Error generating recommendations for {package_name}: {e}")
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return {
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"error": str(e),
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"error_type": type(e).__name__,
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"package_name": package_name,
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"recommendation_type": recommendation_type,
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}
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except Exception as e:
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logger.error(f"Unexpected error generating recommendations for {package_name}: {e}")
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return {
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"error": f"Unexpected error: {e}",
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"error_type": "UnexpectedError",
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"package_name": package_name,
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"recommendation_type": recommendation_type,
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}
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@click.command()
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@click.option(
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"--log-level",
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