Feat/mcp prompt templates #22
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…-making - Add comprehensive prompt templates for package analysis, dependency management, and migration planning - Implement 8 prompt templates covering quality analysis, package comparison, alternative 8000 s suggestion, dependency conflicts, version upgrades, security audits, and migration planning - Add detailed documentation in PROMPT_TEMPLATES.md with usage examples - Include demo script and test coverage for prompt template functionality - Update README.md to highlight new prompt template features - Templates provide structured guidance for common PyPI package scenarios Signed-off-by: longhao <hal.long@outlook.com>
- Implement standard MCP prompt workflow with template variables
- Use {{parameter_name}} placeholders instead of direct string interpolation
- Add proper parameter replacement in server prompt registrations
- Update templates to return template strings with placeholders
- Follow MCP workflow: load template → parameter replacement → return final prompt
- Update documentation to reflect standard MCP workflow implementation
- Remove TEMPLATES_USE environment variable as requested
- Maintain all existing functionality while improving MCP compliance
Signed-off-by: longhao <hal.long@outlook.com>
…lates
Environment Analysis Templates:
- analyze_environment_dependencies: Analyze current Python environment and dependencies
- check_outdated_packages: Check for outdated packages with update priorities
- generate_update_plan: Create comprehensive package update plans with strategies
Trending Analysis Templates:
- analyze_daily_trends: Analyze daily PyPI download trends and popular packages
- find_trending_packages: Discover trending packages over different time periods
- track_package_updates: Track recent package updates and releases
Key Features:
- Follow standard MCP workflow with {{parameter}} template variables
- Support environment analysis (uvx pip list integration ready)
- Enable trending package discovery and popularity analysis
- Provide structured update planning with different strategies
- Include comprehensive parameter validation and documentation
- Add usage examples and integration guides
All templates follow the established MCP prompt workflow:
1. User calls tool → MCP client sends request
2. Tool function executes → Collects necessary data and parameters
3. Call Prompt generator → Pass parameters to corresponding generator
4. Load template → Get template with {{parameter}} placeholders
5. Parameter replacement → Replace {{parameter_name}} with actual values
6. Return final prompt → As tool's response back to AI
Updated documentation and README with new template examples and usage patterns.
Signed-off-by: longhao <hal.long@outlook.com>
- Fix blank line whitespace issues (W293) using ruff --unsafe-fixes - Reformat code using ruff format for consistent styling - Fix analyze_package_quality function to return list[Message] instead of string - Add missing 'assessment' keyword to package analysis template - Update tests to use real prompt functions instead of mocks for structure validation - Fix import ordering in test files - All 64 tests now pass with 47% code coverage Signed-off-by: longhao <hal.long@outlook.com>
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