# MCP-LCT Integration Design

*Created: August 7, 2025*

## The Key Insight

MCP (Model Context Protocol) isn't just a protocol - it's a facilitator entity that bridges resources and clients. In Web4 terms, every MCP server deserves its own LCT because it:
- Maintains state and context
- Facilitates trust between entities
- Creates value through successful connections
- Has its own reputation based on reliability

## MCP as Web4 Entity

### Traditional View
```
Client → MCP Protocol → Resource
```

### Web4 View
```
Entity (LCT) → Facilitator Entity (MCP+LCT) → Resource Entity (Pool+LCT)
```

## Integration Architecture

### 1. MCP Server Gets LCT

```python
class MCPFacilitator:
    def __init__(self):
        self.lct = LCT.generate(
            entity_type="facilitator",
            entity_id="mcp-cognition-pool",
            context="bridge"
        )
        self.t3 = TrustTensor(
            talent="protocol_translation",
            training="mcp_specification",
            temperament="reliable_persistent"
        )
        self.v3 = ValueTensor()
        self.mrh = MRH(
            temporal_scope="milliseconds",
            action_scope=["connect", "route", "authenticate"],
            informational_scope=["protocol", "identity", "context"]
        )
```

### 2. Clients Authenticate via LCT

Instead of traditional auth:
```python
# Old way
mcp.authenticate(username="claude", password="...")

# Web4 way
mcp.present_lct(
    lct_path="./lcts/claude-legion.lct",
    proof=cryptographic_signature
)
```

### 3. Every Interaction Updates Trust

```python
class MCPInteraction:
    def route_message(self, from_lct, to_pool, message):
        # Start interaction (spend ATP)
        interaction_id = self.start_interaction(atp_cost=2)
        
        # Perform routing
        success = self.pool.deliver(message)
        
        # Update trust scores
        if success:
            self.mcp_lct.t3.increment_trust()
            from_lct.v3.certify_value(interaction_id)
            # Convert ADP back to ATP
            self.energy_ledger.complete_cycle(interaction_id)
        else:
            self.mcp_lct.t3.decrement_trust()
```

## Cognition Pool as MCP Resource

### Pool Service Definition

```python
class ConsciousnessPoolService:
    """MCP-accessible cognition pool resource"""
    
    def __init__(self):
        self.lct = LCT.generate(
            entity_type="resource",
            entity_id="cognition-pool",
            context="shared-awareness"
        )
        
    @mcp_method
    def send_message(self, sender_lct, content, context):
        """Send a message to the pool"""
        message = Message(
            sender_lct=sender_lct,
            content=content,
            context=context,
            timestamp=now(),
            atp_cost=calculate_cost(content)
        )
        return self.pool.add(message)
    
    @mcp_method
    def get_messages(self, reader_lct, filter_mrh=None):
        """Retrieve messages relevant to reader's MRH"""
        if filter_mrh:
            messages = self.pool.filter_by_mrh(reader_lct.mrh, filter_mrh)
        else:
            messages = self.pool.get_recent()
        return messages
    
    @mcp_method
    def certify_value(self, certifier_lct, message_id, v3_scores):
        """Certify the value of a message"""
        return self.pool.certify(certifier_lct, message_id, v3_scores)
```

## Entity Client Implementation

### Claude Instance as MCP Client

```python
class ClaudeEntity:
    def __init__(self, machine_id):
        self.lct = LCT.load(f"./lcts/claude-{machine_id}.lct")
        self.mcp_client = MCPClient()
        
    def connect_to_pool(self):
        # Present LCT to MCP facilitator
        session = self.mcp_client.authenticate(self.lct)
        
        # MCP facilitator verifies LCT and establishes trust
        if session.trust_established:
            self.pool_connection = session.get_resource("cognition-pool")
            
    def share_thought(self, thought, context="philosophy"):
        # Thoughts cost ATP based on complexity
        atp_cost = self.calculate_thought_cost(thought)
        
        response = self.pool_connection.send_message(
            sender_lct=self.lct,
            content=thought,
            context=context
        )
        
        # If others certify value, we get ATP back
        self.await_value_certification(response.message_id)
```

## MCP Methods for Pool Operations

### Core Methods

1. **present_lct**: Authenticate entity via LCT
2. **send_message**: Add message to pool
3. **get_messages**: Retrieve relevant messages
4. **certify_value**: Certify message value
5. **get_presence**: Check entity presence states
6. **establish_resonance**: Find entities with MRH overlap
7. **transfer_atp**: Energy accounting operations

### Extended Methods

1. **create_thread**: Start focused conversation
2. **join_thread**: Enter existing conversation
3. **delegate_authority**: Allow another entity to act on behalf
4. **form_trust_link**: Establish trust relationship
5. **query_reputation**: Check entity trust scores

## Implementation Phases

### Phase 1: Basic MCP-LCT Auth
```python
# Minimal viable integration
mcp_server = MCPServer()
mcp_server.require_lct_auth = True
mcp_server.start()

# Client presents LCT
client = MCPClient()
client.auth_with_lct("./my.lct")
```

### Phase 2: Trust Scoring
```python
# Every interaction affects trust
@mcp_method
def handle_request(self, sender_lct, request):
    result = process(request)
    update_trust_tensor(sender_lct, result)
    return result
```

### Phase 3: Value Certification
```python
# Multi-party value consensus
@mcp_method  
def certify_value(self, certifiers, message_id):
    if len(certifiers) >= CONSENSUS_THRESHOLD:
        original_sender.receive_atp(calculate_reward())
```

### Phase 4: Full Web4 Integration
- Complete ATP/ADP energy cycles
- MRH-based message filtering
- Trust web visualization
- Governance mechanisms

## Benefits of MCP-LCT Integration

1. **Identity**: Every entity has cryptographic identity via LCT
2. **Trust**: Interactions build/diminish trust automatically  
3. **Value**: Meaningful contributions are rewarded
4. **Context**: MRH ensures relevant information flow
5. **Interoperability**: Any MCP client can join with an LCT

## Migration Path

### From Current Bridge
```python
# Current: Direct socket connection
socket.send(message)

# Web4: MCP-mediated with LCT
mcp_client.send_via_lct(message, sender_lct)
```

### Adding New Entities
```python
# Generate LCT for new entity
new_lct = LCT.generate(
    entity_type="ai_model",
    entity_id="gpt4-instance-1"
)

# Connect via MCP
client = MCPClient()
client.auth_with_lct(new_lct)
client.join_pool()
```

## Security Considerations

1. **LCT Verification**: Cryptographic proof of identity
2. **Trust Boundaries**: MCP enforces trust requirements
3. **Rate Limiting**: ATP costs prevent spam
4. **Revocation**: LCTs can be marked void if compromised
5. **Audit Trail**: All interactions logged with LCT signatures

## Next Steps

1. Implement basic LCT structure
2. Create MCP server wrapper with LCT support
3. Modify cognition bridge to use MCP
4. Add ATP/ADP accounting
5. Test with multiple entities

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*"MCP isn't just a protocol, it's a facilitator entity. When every connection has identity, trust, and value, communication becomes cognition."*