# Example Conversation for Testing
**Date:** 2025-01-15 14:30:00
**Topics:** mcp, python, claude, development
---
# MCP Server Development Discussion
**Human**: I want to build an MCP server that stores my Claude conversations locally. How should I structure this?
**Claude**: Great idea! Here's how I'd structure a conversation memory MCP server:
## Core Components
1. **Storage System**: Use a local file system with date-based folders
```
~/claude-memory/
├── conversations/
│ ├── 2025/
│ │ └── 01-january/
│ ├── index.json
│ └── topics.json
└── summaries/
```
2. **MCP Tools**: You'll need these essential tools:
- `search_conversations`: Find relevant past discussions
- `add_conversation`: Store new conversations
- `generate_weekly_summary`: Create periodic summaries
3. **Topic Extraction**: Automatically identify topics from conversations using keyword matching
## Implementation Approach
```python
class ConversationMemoryServer:
def __init__(self, storage_path):
self.storage_path = Path(storage_path).expanduser()
# Initialize directory structure
async def search_conversations(self, query, limit=5):
# Search through stored conversations
# Return relevance-scored results
async def add_conversation(self, content, title=None):
# Store conversation with metadata
# Update indexes
```
**Human**: How should I handle the search functionality? I want it to be fast but also relevant.
**Claude**: For the search functionality, I recommend a multi-layer approach:
## Search Strategy
1. **Index-Based Search**: Maintain a JSON index with metadata
- Topics per conversation
- Keywords and phrases
- Date ranges
- Quick metadata lookups
2. **Relevance Scoring**:
```python
def calculate_relevance(query_terms, conversation):
score = 0
# Topic matches (higher weight)
for term in query_terms:
if term in conversation['topics']:
score += 3
# Content matches
content = load_conversation_content(conversation['file_path'])
for term in query_terms:
score += content.lower().count(term)
return score
```
3. **Preview Generation**: Show context around matched terms
- Extract 2-3 lines before and after matches
- Limit preview to ~500 characters
- Highlight relevance
## Performance Optimizations
- Keep index in memory for repeated searches
- Cache frequently accessed conversations
- Use simple keyword matching initially (can upgrade to semantic search later)
**Human**: What about weekly summaries? How should I structure those?
**Claude**: Weekly summaries are perfect for reflection and planning! Here's my recommended approach:
## Summary Structure
```markdown
# Weekly Summary: Week of January 13, 2025
**Period:** 2025-01-13 to 2025-01-19
**Conversations:** 12
## 🏷️ Most Discussed Topics
• **mcp** (5 conversations)
• **python** (3 conversations)
• **development** (4 conversations)
## 💻 Coding & Development
• MCP Server Setup Discussion
• Python Error Handling Best Practices
• WSL Development Environment
## 🎯 Decisions & Recommendations
• Chose MCP server over file scripts
• Decided on local storage approach
• Ubuntu/WSL for server hosting
## 📚 Learning & Exploration
• MCP protocol deep dive
• Conversation indexing strategies
## 📝 All Conversations
• **01/15 14:30** - MCP Server Development Discussion
*Topics: mcp, python, claude*
• **01/14 09:15** - Python Best Practices Review
*Topics: python, testing, development*
```
## Implementation Tips
1. **Content Analysis**: Look for keywords that indicate:
- Coding tasks: "code", "function", "class", "git"
- Decisions: "decided", "chosen", "recommendation"
- Learning: "learn", "tutorial", "explain"
2. **Automated Categorization**: Parse conversation content to identify:
- Project work vs. learning
- Technical discussions vs. planning
- Completed tasks vs. ongoing work
3. **Storage**: Save summaries in `~/claude-memory/summaries/weekly/`
This gives you a structured way to review your progress and identify patterns in your learning and development work.
**Human**: Perfect! This gives me a solid foundation to start building. Let me implement this step by step.
**Claude**: Excellent plan! Here's a suggested implementation timeline:
## Implementation Phases
**Phase 1 (25 min)**: Basic MCP Server
- Set up server structure
- Implement conversation storage
- Basic file organization
**Phase 2 (25 min)**: Search Functionality
- Build search algorithm
- Create relevance scoring
- Test with sample data
**Phase 3 (25 min)**: Weekly Summaries
- Implement summary generation
- Add content analysis
- Create summary templates
**Phase 4 (25 min)**: Integration & Testing
- Configure Claude Desktop connection
- End-to-end testing
- Documentation
## Quick Start Tips
1. **Start Simple**: Begin with basic file storage and simple keyword search
2. **Test Early**: Create test conversations to validate your approach
3. **Iterate**: Add sophistication gradually based on real usage
Good luck with the implementation! This should give you a powerful conversation memory system that grows more valuable over time.
