Stdio-first Model Context Protocol (MCP) server that pairs a TensorFlow.js-backed memory model with optional online learning and workflow orchestration services. This README reflects the project state as of October 10, 2025 and aligns with the current src/index.ts implementation.

• Node.js 22.0.0+ (enforced by package.json engines field)
• npm (bundled with Node) or pnpm/yarn if you prefer to adapt the scripts
• Python build chain / node-gyp prerequisites suitable for installing @tensorflow/tfjs-node
• Optional: network access for dataset downloads invoked by the training scripts in scripts/

git clone https://github.com/henryhawke/mcp-titan.git
cd mcp-titan
npm install
npm run build
npm start   # launches the MCP server over stdio

The runtime creates ~/.titan_memory/ (override with --memoryPath when instantiating TitanMemoryServer) and auto-saves the active memory state every 60 seconds.

The published package exposes a titan-memory binary (see bin in package.json). Use it directly or via npx:

npx titan-memory

The server only speaks MCP over stdio. Point your client at the executable instead of an HTTP URL.

{
  "mcp": {
    "servers": {
      "titan-memory": {
        "command": "titan-memory",
        "env": {
          "NODE_ENV": "production"
        },
        "workingDirectory": "~/.titan_memory"
      }
    }
  }
}

Add a custom MCP server, choose “Run a binary on this machine,” and point it at the same titan-memory command. No port configuration is required.

Use the prompt below (or the version tracked in docs/llm-system-prompt.md) when wiring Titan into Cursor, Claude, or other MCP-compatible agents.

You are operating the @henryhawke/mcp-titan MCP memory server. Follow this checklist on every session:

1. **Discover** — Call `help` to confirm the active tool registry and read parameter hints.
2. **Initialize** — If the model was not auto-loaded, invoke `init_model` with any config overrides. For momentum tuning, adjust:
   - `momentumLearningRate` (base θ),
   - `momentumScoreGain` / `momentumScoreToDecay` (attention-weighted scaling),
   - `momentumSurpriseGain` (surprise-driven boost), and
   - `momentumScoreFloor` (stability floor).
3. **Prime Memory** — Use `bootstrap_memory` or `train_step` pairs before running `forward_pass`. Inspect `memory_stats` / `get_memory_state` to verify momentum and forgetting gates are active.
4. **Maintain** — When utilization exceeds ~70%, run `prune_memory`; persist progress with `save_checkpoint` and restore via `load_checkpoint` after restarts.
5. **Learn Online** — Manage the learner loop through `init_learner`, `pause_learner`, `resume_learner`, `get_learner_stats`, and feed data with `add_training_sample`.
6. **Observe** — Pull telemetry using `get_token_flow_metrics`, `health_check`, and (when hierarchical memory is enabled) `get_hierarchical_metrics`.

Always treat tool responses as authoritative and surface any error text back to the user with context.

• Stdio MCP Server: Implemented with McpServer + StdioServerTransport; no HTTP layer is active in this release.
• Auto-Initialization: Loads existing checkpoints from ~/.titan_memory or bootstraps a default config (inputDim=768, memorySlots=5000, transformerLayers=6).
• Memory Management: Vector processing, TF-IDF bootstrap helper, gradient reset, and information-gain pruning when supported by the model.
• Online Learner Loop: LearnerService exposes MCP hooks for replay-buffer based updates. A mock tokenizer is installed by default—swap in AdvancedTokenizer for real encodings.
• Workflow Skeleton: src/workflows/ contains orchestrators for release automation, linting, and feedback analysis. These rely on WorkflowConfig feature flags defined in src/types.ts.
• Training Pipeline: scripts/train-model.ts pipes into src/training/trainer.ts, providing synthetic data generation and tokenizer training when no dataset is supplied.

The server currently registers 19 MCP tools:

help, bootstrap_memory, init_model, memory_stats, forward_pass, train_step, get_memory_state, get_token_flow_metrics, get_hierarchical_metrics, reset_gradients, health_check, prune_memory, save_checkpoint, load_checkpoint, init_learner, pause_learner, resume_learner, get_learner_stats, add_training_sample.

The help tool now derives its listing dynamically from the active registry, so invoke it at runtime to confirm newly added operations. See docs/api/README.md for parameter schemas and defaults. The manifold_step operation remains on the roadmap—track progress in ROADMAP_ANALYSIS.md.

• Memory state is serialized to ~/.titan_memory/memory_state.json.
• Saved models live under ~/.titan_memory/model/.
• MCP checkpoints written via save_checkpoint are user-specified JSON files that include tensor shapes for validation on load.
• Auto-save interval: 60 seconds with exponential back-off retry on transient errors.

• npm run test uses Jest (make sure to enable experimental modules for Node 22).
• npm run train-model, npm run train-quick, npm run train-production call into the training pipeline described above.
• npm run download-data fetches example corpora (WikiText-2, TinyStories, OpenWebText sample) and supports synthetic data generation when offline.

• Help text still references manifold_step; update server messaging once the roadmap decision (implement vs remove) finalizes.
• manifold_step and several advanced memory operations are referenced in strings but lack MCP tool bindings.
• Additional roadmap tools (encode_text, get_surprise_metrics, analyze_memory, predict_next) have schemas drafted but no handlers yet—track status in SYSTEM_AUDIT.md.
• src/tests/ folder is absent; existing automated coverage resides under test/ and should be expanded/migrated.
• Tensor summarization in bootstrap_memory uses heuristics; integrate an actual summarizer or reuse the tokenizer pipeline for higher fidelity.
• Review workflow managers (src/workflows/) for real credential handling before production use.

• docs/api/README.md — complete tool and parameter reference.
• docs/architecture-overview.md — component relationships and data flow.
• docs/setup-and-tools-guide.md — exhaustive setup checklist and tool walkthrough.
• SYSTEM_AUDIT.md — consolidated audit and status tracker.
• IMPLEMENTATION_PACKAGE.md — navigation guide for research and production tasks.
• IMPLEMENTATION_COMPLETE.md — current delivery scope and outstanding tasks.
• ROADMAP_ANALYSIS.md — strategic outlook and feature roadmap.