ANALYSIS_SUMMARY.md•2.77 kB
# MCP-Titan Memory Server Analysis Summary
**Date:** October 10, 2025
**Version Audited:** 3.0.0
**Server Identity:** `Titan Memory` v1.2.0 (stdio MCP)
---
## Highlights
- TypeScript ESM wiring, TensorFlow.js integration, and MCP transport now run cleanly on Node 22+ (`npm run build`, `npm start`).
- Auto-initialization creates/loads state under `~/.titan_memory/`, with 60 s auto-save and guarded retries (`src/index.ts:974`).
- Input validation, path sanitization, and checkpoint dimension checks are in place (`src/index.ts:943`, `src/index.ts:541`).
- Documentation set refreshed to match the current implementation (see `README.md`, `docs/api/README.md`).
- Advanced research hooks (momentum, token flow, hierarchical memory) remain stubs and are tracked in `ROADMAP_ANALYSIS.md`.
---
## Available MCP Tools
Registered tools (15) and their primary responsibilities are documented in `docs/api/README.md`. Quick reference:
`help`, `bootstrap_memory`, `init_model`, `memory_stats`, `forward_pass`, `train_step`, `get_memory_state`, `reset_gradients`, `prune_memory`, `save_checkpoint`, `load_checkpoint`, `init_learner`, `pause_learner`, `resume_learner`, `get_learner_stats`, `add_training_sample`.
> The help text still mentions `manifold_step`; the tool is not registered. Implement or remove it to eliminate confusion (tracked in `ROADMAP_ANALYSIS.md`).
Learner tools rely on a mock tokenizer that produces random vectors. Replace `TitanMemoryServer.tokenizer` with `AdvancedTokenizer` for deterministic embeddings before using training workflows (`src/index.ts:706`).
---
## Integration Checklist (Cursor / Claude Desktop)
1. **Install dependencies**
```bash
npm install
npm run build
```
2. **Launch the server** via stdio:
```bash
npm start # or
npx titan-memory # uses the published bin
```
3. **Configure client** to execute the binary (no HTTP endpoint):
```json
{
"mcp": {
"servers": {
"titan-memory": {
"command": "titan-memory",
"env": { "NODE_ENV": "production" },
"workingDirectory": "~/.titan_memory"
}
}
}
}
```
4. **Usage order:** call `help` → `init_model` before inference or training tools. Use `save_checkpoint` / `load_checkpoint` for persistence.
---
## Outstanding Items
- Wire up research features (momentum, token flow, hierarchical memory) per `DETAILED_IMPLEMENTATION_GUIDE.md`.
- Register or remove `manifold_step` to sync tooling with documentation.
- Replace learner mock tokenizer and expand test coverage for training workflows.
- Add structured logging/telemetry surface once stability work begins.
Reviews and further planning: `ROADMAP_ANALYSIS.md`, `IMPLEMENTATION_PROGRESS.md`.