GEDCOM MCP Server

# GEDCOM MCP Server - Improvement Plan This document outlines the improvements and future development plans for the GEDCOM MCP server, based on our recent analysis and discussions. ## Current Status The GEDCOM MCP server is a functional tool for querying genealogical data from GEDCOM files through the Model Control Protocol (MCP). It provides a comprehensive set of tools for: - Loading and parsing GEDCOM files - Searching and querying people, families, events, and places - Managing genealogical data (adding, updating, removing records) - Analyzing genealogical data (statistics, duplicates, timelines) - Finding relationships between individuals Recent fixes have addressed critical bugs and improved code quality while maintaining all existing functionality. ## Key Improvement Areas ### 1. Enhanced Search Capabilities #### Fuzzy Search **Problem**: Exact string matching can miss records due to typos or name variations **Solution**: Implement fuzzy search using libraries like `fuzzywuzzy` or `python-Levenshtein` ```python @mcp.tool() async def fuzzy_search_person(name: str, ctx: Context, threshold: int = 80) -> list: """Search for persons with fuzzy name matching""" # Implementation using fuzzy string matching pass ``` **Benefits**: - Find records despite typos in names - Handle name variations (e.g., "Jon" vs "John") - Improve search recall while maintaining precision ### 2. Progress Indicators #### Long-Running Operations **Problem**: Large GEDCOM files can take significant time to load/parse with no user feedback **Solution**: Implement progress tracking for operations ```python class ProgressTracker: def __init__(self, total_items: int, description: str): self.total_items = total_items self.processed = 0 self.description = description def update(self, increment: int = 1): # Report progress at regular intervals pass ``` **Benefits**: - Better user experience for large datasets - Visibility into system status - Ability to estimate completion times ### 3. Data Validation Tools #### Data Integrity Checks **Problem**: GEDCOM files may contain inconsistent or invalid data **Solution**: Comprehensive validation tools ```python @mcp.tool() async def validate_gedcom_data(ctx: Context) -> dict: """Validate GEDCOM data integrity and consistency""" # Check for: # - Invalid dates (future dates, impossible lifespans) # - Inconsistent relationships # - Orphaned records # - Missing required fields pass ``` **Benefits**: - Identify data quality issues - Help users clean their genealogical data - Prevent errors in downstream processing ### 4. Enhanced Duplicate Detection #### Sophisticated Matching **Problem**: Current duplicate detection is basic **Solution**: Advanced matching algorithms **Benefits**: - Reduce false positives/negatives - Handle complex name variations - Consider multiple data points (dates, places, relationships) ### 5. Better Error Handling #### Recovery-Oriented Error Messages **Problem**: Generic error messages don't help users resolve issues **Solution**: Structured errors with recovery suggestions ```python class GedcomError(Exception): def __init__(self, message: str, error_code: str, recovery_suggestion: str): self.message = message self.error_code = error_code self.recovery_suggestion = recovery_suggestion super().__init__(self.message) ``` **Benefits**: - Help users understand and resolve errors - Provide actionable next steps - Enable automated error recovery in some cases ### 6. Batch Operations #### Efficient Bulk Processing **Problem**: Performing operations on many records requires multiple API calls **Solution**: Batch operation tools ```python @mcp.tool() async def batch_update_person_attributes(updates: list, ctx: Context) -> dict: """Update multiple person attributes in a single operation""" # More efficient than multiple individual updates pass ``` **Benefits**: - Reduced API overhead - Better performance for bulk operations - Atomic operations with rollback capabilities ### 7. Advanced Querying #### Complex Data Filtering **Problem**: Current query capabilities are limited **Solution**: Rich query language support ```python @mcp.tool() async def query_people_advanced(ctx: Context, query: dict) -> dict: """Advanced querying with complex conditions""" # Support for: # - Logical operators (AND, OR, NOT) # - Comparison operators ($gt, $lt, $eq, etc.) # - Regular expressions # - Nested field queries pass ``` **Benefits**: - More precise data retrieval - Complex analytical queries - Better integration with AI agent workflows ### 8. Relationship Analysis #### Advanced Relationship Tools **Problem**: Basic relationship finding is limited **Solution**: Sophisticated relationship analysis ```python @mcp.tool() async def analyze_family_connections(person_ids: list, ctx: Context) -> dict: """Analyze connection patterns between multiple people""" # Features: # - Common ancestors # - Connection strength metrics # - Cluster analysis # - Shortest path analysis pass ``` **Benefits**: - Deeper genealogical insights - Research assistance for complex family histories - Network analysis capabilities ## Implementation Priorities ### High Priority 1. **Better Error Handling** - Immediate user experience improvement 2. **Enhanced Duplicate Detection** - Data quality improvement 3. **Progress Indicators** - User experience for large datasets ### Medium Priority 1. **Fuzzy Search** - Search capability enhancement 2. **Data Validation Tools** - Data quality assurance 3. **Batch Operations** - Performance improvement ### Low Priority 1. **Advanced Querying** - Feature enhancement 2. **Relationship Analysis** - Specialized functionality 3. **Comprehensive Type Hints** - Code quality improvement ## Technical Considerations ### Performance - Implement caching strategies for expensive operations - Use lazy loading for large datasets - Consider asynchronous processing for long-running operations ### Security - Validate all inputs to prevent injection attacks - Implement proper access controls if multi-user support is added - Sanitize data before returning to clients ### Maintainability - Add comprehensive type hints - Improve code documentation - Follow consistent coding standards - Implement proper logging ## Dependencies and Requirements ### New Dependencies - `fuzzywuzzy` or `python-Levenshtein` for fuzzy matching - `rapidfuzz` for better performance fuzzy matching - Additional testing frameworks for new functionality ### Infrastructure - Consider memory requirements for large datasets - Plan for scaling if server usage increases - Ensure compatibility with different Python versions ## Testing Strategy ### Unit Tests - Test each new function independently - Mock external dependencies - Test edge cases and error conditions ### Integration Tests - Test complete workflows - Verify data consistency across operations - Test performance with large datasets ### Regression Tests - Ensure existing functionality remains intact - Monitor performance impacts - Validate error handling improvements ## Documentation Needs ### API Documentation - Document all new tools and functions - Provide examples for common use cases - Specify parameter requirements and return types ### User Guides - Explain new features and how to use them - Provide troubleshooting guidance - Include best practices for data management ## Success Metrics ### Quantitative - Reduction in user-reported errors - Improvement in search result quality - Performance improvements for large datasets - Increase in successful data operations ### Qualitative - Improved user satisfaction - Better data quality reports - More efficient research workflows - Enhanced AI agent capabilities ## Risks and Mitigation ### Technical Risks - **Performance Impact**: New features might slow down existing operations - *Mitigation*: Profile changes and optimize critical paths - **Compatibility Issues**: New dependencies might conflict with existing ones - *Mitigation*: Test thoroughly in isolated environments ### Implementation Risks - **Scope Creep**: Features might become more complex than planned - *Mitigation*: Implement in small, incremental steps - **Resource Constraints**: Development time might be limited - *Mitigation*: Prioritize high-impact features first ## Next Steps 1. **Immediate**: Implement better error handling with recovery suggestions 2. **Short-term**: Add fuzzy search capabilities and progress indicators 3. **Medium-term**: Enhance duplicate detection and add batch operations 4. **Long-term**: Implement advanced querying and relationship analysis This improvement plan focuses on enhancing the core value proposition of the GEDCOM MCP server as a powerful tool for AI agents working with genealogical data, rather than replicating traditional application features.