# MCP Server Setup Complete ✅

The MONIKA MCP server has been successfully implemented and is ready to use!

## What Was Added

### 1. MCP Server Implementation
**File**: `salience_os_seed/runtime/mcp_server.py`

A complete MCP server exposing MONIKA's capabilities:
- Memory operations (add facts, todos, hypotheses)
- Introspection (yearning state, controller dynamics)
- Runtime control (steps, training, response generation)
- State management (save/load checkpoints)

### 2. Server Launcher
**File**: `start_mcp_server.py`

Easy-to-use entry point:
```bash
python start_mcp_server.py
python start_mcp_server.py --state storage/conversation_state.json
python start_mcp_server.py --checkpoint storage/proto_lm/dolly15k.pt
```

### 3. Documentation
**File**: `docs/MCP_SERVER.md`

Comprehensive guide covering:
- Installation and setup
- Available tools and their usage
- Bidirectional architecture explanation
- Example use cases
- Troubleshooting

### 4. Example Configuration
**File**: `examples/claude_desktop_config.json`

Ready-to-use Claude Desktop configuration

### 5. Test Client
**File**: `examples/test_mcp_client.py`

Programmatic testing tool:
```bash
# Run automated tests
python examples/test_mcp_client.py

# Interactive REPL mode
python examples/test_mcp_client.py --interactive
```

## Quick Start

### Option A: Connect from Claude Desktop

1. Install MCP SDK:
```bash
pip install mcp
```

2. Add to Claude Desktop config (`%APPDATA%\Claude\claude_desktop_config.json`):
```json
{
  "mcpServers": {
    "monika": {
      "command": "python",
      "args": ["C:\\MONIKA\\start_mcp_server.py", "--state", "storage/conversation_state.json"]
    }
  }
}
```

3. Restart Claude Desktop

4. MONIKA tools will appear in your tool panel!

### Option B: Test Locally

```bash
# Run test suite
python examples/test_mcp_client.py

# Or interactive mode
python examples/test_mcp_client.py --interactive
```

## Available Tools

Once connected, you'll have access to:

- `yearning_state()` - See what MONIKA desires/needs right now
- `controller_dynamics()` - View decision-making state
- `memory_snapshot()` - Read facts/todos/hypotheses
- `memory_apply(verb)` - Add memories
- `runtime_step(text)` - Execute processing step
- `training_step(text)` - Train on new text
- `generate_response(prompt)` - Generate text
- `adjust_controller_dynamics(updates)` - Tune behavior
- `get_training_metrics()` - Check training status
- `save_state(path)` - Persist state

## Bidirectional Loop

**Claude → MONIKA:**
```python
# Monitor yearning
yearning = monika.yearning_state()

# Inject training based on what it needs
if yearning["novelty"]["desire"] > 0.8:
    monika.training_step(novel_corpus)

# Adjust weights when learning plateaus
monika.adjust_controller_dynamics({
    "novelty_weight": 0.05,
    "drag_penalty": -0.03
})
```

**MONIKA → Claude:**
- Uses Claude as MCP tool client for web search, file ops, etc.
- MONIKA's existing `tool_adapter` already supports MCP tools

## Safety Notes

⚠️ **Training interference**: The MCP server creates its own session. If you have a training run in another process, they won't share state unless both point to the same checkpoint file.

✅ **Read-only tools**: `yearning_state`, `memory_snapshot`, `controller_dynamics` are safe to call anytime - they won't modify state.

⚠️ **Write operations**: `training_step`, `adjust_controller_dynamics`, `memory_apply` will modify the model. Use carefully during active training.

## Architecture Overview

```
┌─────────────────────────────────────────────────────────┐
│                     Claude Desktop                       │
│                     (MCP Client)                         │
└────────────────────┬────────────────────────────────────┘
                     │ MCP Protocol (stdio)
                     │
┌────────────────────▼────────────────────────────────────┐
│              start_mcp_server.py                         │
│           (MCP Server Entry Point)                       │
└────────────────────┬────────────────────────────────────┘
                     │
┌────────────────────▼────────────────────────────────────┐
│      salience_os_seed/runtime/mcp_server.py             │
│              (MONIKAMCPServer)                          │
│                                                          │
│  ┌──────────────────────────────────────────────────┐   │
│  │  Tool Handlers (call_tool)                       │   │
│  │  - memory_apply, memory_snapshot                 │   │
│  │  - yearning_state, controller_dynamics           │   │
│  │  - runtime_step, training_step                   │   │
│  │  - adjust_controller_dynamics                    │   │
│  └───────────┬──────────────────────────────────────┘   │
│              │                                           │
└──────────────┼───────────────────────────────────────────┘
               │
   ┌───────────▼─────────────┬─────────────────────────┐
   │                         │                         │
┌──▼─────────────────┐ ┌────▼──────────────────┐ ┌───▼────────────────┐
│ ConversationSession│ │  SalienceRuntime      │ │ ProtoLanguageModel │
│  (conversation/)   │ │  (runtime/)           │ │   (proto_lm/)      │
└────────────────────┘ └───────────────────────┘ └────────────────────┘
         │                      │                          │
         └──────────┬───────────┴──────────────────────────┘
                    │
         ┌──────────▼──────────────────────────────────────┐
         │         Existing Bridge Resources                │
         │  - MemoryResource (mcp_bridge.py)               │
         │  - IntrospectionResource (mcp_bridge.py)        │
         │  - SessionOrchestrator (mcp_bridge.py)          │
         └─────────────────────────────────────────────────┘
```

## What This Enables

### 1. Supervised Training
Monitor MONIKA's yearning state in real-time and inject exactly what it needs when it needs it.

### 2. Interactive Debugging
Inspect controller dynamics, adjust weights on the fly, and understand why MONIKA makes specific decisions.

### 3. Curriculum Learning
Orchestrate multi-stage training by watching salience metrics and advancing when the model saturates.

### 4. Hybrid Intelligence
MONIKA uses Claude for tools → Claude supervises MONIKA's training → Creates a mutual learning loop.

### 5. Research Transparency
Full introspection into the salience-driven decision process - you can see exactly what the system is optimizing for.

## Next Steps

1. **Test it locally**:
   ```bash
   python examples/test_mcp_client.py
   ```

2. **Connect from Claude Desktop** using the example config

3. **Start experimenting**:
   - Ask Claude to monitor MONIKA's yearning state
   - Have Claude inject training based on salience signals
   - Let Claude adjust controller dynamics during training

4. **Watch the bidirectional loop** emerge naturally

## Troubleshooting

See `docs/MCP_SERVER.md` for detailed troubleshooting steps.

Common issues:
- "MCP SDK not installed" → `pip install mcp`
- Server not appearing in Claude → Check config JSON syntax, restart Claude Desktop
- Import errors → Make sure you're running from MONIKA root directory

## Files Modified

**None!** All additions are new files - your training run is completely untouched.

---

**The MCP bridge is live. MONIKA can now be supervised by Claude, and Claude can be used as a tool by MONIKA. The bidirectional intelligence loop is ready to activate. 🚀**