MCP Titan

# Implementation Status Summary Updated October 10, 2025 following the Phase 0 audit and documentation realignment. This file replaces legacy "mission accomplished" messaging with an accurate snapshot of what currently ships in `@henryhawke/mcp-titan`. ## Delivery Scope - **MCP Server:** `TitanMemoryServer` exposes 17 stdio tools covering discovery, onboarding, inference, observability, persistence, and learner management. See [docs/api/README.md](docs/api/README.md) for schemas. - **Model Core:** `TitanMemoryModel` implements transformer-style memory updates, telemetry, information-gain pruning hooks, and persistence helpers. - **Learner Loop:** `LearnerService` offers replay-buffer based online updates with configurable loss weights and gradient accumulation. - **Training Scripts:** `scripts/train-model.ts` and `src/training/trainer.ts` can generate synthetic corpora, train the tokenizer, and persist model artifacts. - **Workflow Scaffolding:** `src/workflows/` provides orchestrators for release automation, linting, and feedback collection—intended as starting points, not production-ready modules. ## What Works Today - Node 22+ build pipeline (`npm run build`) emits `dist/` artifacts consumed by `index.js`. - MCP clients (Cursor, Claude Desktop) can connect over stdio using the `titan-memory` binary. - Auto-initialization creates or reloads model artifacts and memory state under `~/.titan_memory/`. - `prune_memory`, `save_checkpoint`, and `load_checkpoint` enforce path safety and tensor shape validation. - Learner controls (`init_learner`, `pause_learner`, `resume_learner`, `get_learner_stats`, `add_training_sample`) operate with the built-in mock tokenizer. - Training CLI can complete an end-to-end synthetic run, producing model weights in `trained_models/`. ## Known Limitations - `manifold_step` (and related advanced memory hooks) remain roadmap items with no MCP handlers; documentation calls this out explicitly. - Learner mock tokenizer generates random vectors; replace with `AdvancedTokenizer` before attempting to learn from real corpora. - `bootstrap_memory` summarization is heuristic-only; results may be noisy on larger documents. - Workflow managers assume valid GitHub credentials and do not yet implement resilient retry/backoff logic. - Automated tests live in `test/` but do not cover the learner or workflow subsystems; `src/tests/` is absent. ## Validation Checklist - [x] `npm run build` - [x] `npm start` launches stdio MCP server - [x] `init_model` / `forward_pass` / `train_step` round-trip tensors without leaks (validated manually via logging) - [x] `prune_memory` returns stats from `model.getPruningStats()` when information-gain pruning exists - [x] `save_checkpoint` → `load_checkpoint` cycle verified against sample JSON output - [ ] Learner loop trained with deterministic tokenizer (pending) - [ ] Workflow orchestrator exercised against live GitHub API (pending) - [ ] Structured integration tests over MCP transport (pending) ## Recommended Next Actions 1. Register or remove `manifold_step` to eliminate tooling drift. 2. Swap the learner tokenizer for `AdvancedTokenizer` and add smoke tests for `add_training_sample`. 3. Expand automated coverage—start with stdio-driven MCP integration tests. 4. Harden workflow managers for production credentials (rate limiting, retries, observability). For broader strategy, refer to [ROADMAP_ANALYSIS.md](ROADMAP_ANALYSIS.md). For component diagrams and dependencies see [docs/architecture-overview.md](docs/architecture-overview.md).