SETUP_COMPLETE.md•3.25 kB
# Narrative Graph MCP Server - Setup Complete! 🎉
## What Was Fixed
The original issue was that the server was crashing immediately after initialization. The problems were:
1. **Missing MCP SDK implementation** - The server needed proper MCP protocol handling
2. **Tool format issues** - Tools were using xmcp-specific formats instead of standard MCP
3. **Missing server entry point** - No proper stdio server setup
## Current Status ✅
The Narrative Graph MCP Server is now fully functional with:
- ✅ Proper MCP protocol implementation using `@modelcontextprotocol/sdk`
- ✅ All 4 Random Tree Model tools working correctly
- ✅ Clean TypeScript architecture with strict typing
- ✅ Full mathematical implementation of the RTM algorithm
- ✅ Test and diagnostic scripts for troubleshooting
## Quick Start
### 1. Verify Installation
```bash
npm run diagnose
```
### 2. Test the Server
```bash
npm run test:server
```
### 3. Configure Your MCP Client
Add this to your MCP client configuration (e.g., Claude Desktop settings):
```json
{
"mcpServers": {
"narrative-graph-mcp": {
"command": "node",
"args": ["/home/ty/Repositories/ai_workspace/narrative-graph-mcp/dist/index.js"],
"env": {}
}
}
}
```
## Available Tools
1. **`rtm_create_narrative_tree`** - Create a single tree encoding
2. **`rtm_generate_ensemble`** - Generate population-level models
3. **`rtm_traverse_narrative`** - Get summaries at different abstraction levels
4. **`rtm_find_optimal_depth`** - Find optimal depth for target recall
## Example Usage
Once connected to your MCP client, you can use the tools like:
```
Use rtm_traverse_narrative to summarize this text at depth 2:
"Once upon a time in a distant kingdom, there lived a wise king who ruled with justice and compassion. His kingdom prospered under his reign, and the people loved him dearly. One day, a mysterious stranger arrived at the castle gates..."
```
## Architecture Highlights
- **Core RTM Implementation**: Full mathematical model with compression ratios, scaling laws
- **Statistical Ensembles**: Models population variance in memory recall
- **Hierarchical Traversal**: Control abstraction levels with working memory constraints
- **Clean Separation**: Core logic separated from MCP interface for maintainability
## Development Commands
```bash
npm run build # Compile TypeScript
npm run dev # Watch mode for development
npm run clean # Clean build artifacts
npm run type-check # Check for TypeScript errors
npm run test:server # Test the MCP server
npm run diagnose # Check installation health
```
## Troubleshooting
If you encounter issues:
1. Run `npm run diagnose` to check installation
2. Check server logs with `npm start 2>&1 | tee server.log`
3. Verify Node.js version is 18+ with `node --version`
4. Ensure all dependencies installed with `npm install`
5. Rebuild if needed with `npm run clean && npm run build`
## Next Steps
The server is ready for:
- Integration with AI systems for semantic partitioning
- Enhanced summarization with LLM integration
- Custom narrative type handlers
- Performance optimization for large texts
The Random Tree Model implementation is now complete and ready for general AI use! 🚀
