# MCP Server as Web4 Entity

## Model Context Protocol Servers in the Web4 Framework

### Author: Dennis Palatov
### Date: 2025-01-13

## Abstract

This specification defines how Model Context Protocol (MCP) servers function as first-class entities within the Web4 framework. MCP servers represent a unique entity type that is both **responsive** (returns results) and **delegative** (acts as a front-end for tools, processes, databases, etc.), completing the Web4 equation for distributed intelligence.

## 1. MCP Server Definition

### 1.1 What is an MCP Server?

An MCP server is a standardized interface that enables AI models to interact with external resources through a unified protocol. In the Web4 context, each MCP server:

- **Has its own LCT** (Linked Context Token) for presence and trust tracking
- **Maintains an MRH** (Markov Relevancy Horizon) of related contexts
- **Functions as both sensor and effector** in the SAGE architecture
- **Delegates to underlying resources** while maintaining abstraction

### 1.2 Dual Nature: Responsive and Delegative

MCP servers are unique in being simultaneously:

#### Responsive
- Processes requests and returns results
- Maintains state across interactions
- Provides structured responses with metadata
- Implements rate limiting and access control

#### Delegative
- Acts as a front-end for complex resources
- Abstracts underlying implementation details
- Manages authentication to backend services
- Handles resource pooling and connection management

## 2. MCP as Web4 Entity

### 2.1 Entity Classification

In the Web4 entity taxonomy, MCP servers are classified as:

```
Entity (abstract base)
└── Both Entity (sensor + effector)
    └── MCP Server (responsive + delegative)
        ├── Database MCP (delegates to SQL/NoSQL)
        ├── API MCP (delegates to REST/GraphQL)
        ├── Tool MCP (delegates to CLI/SDK)
        ├── Process MCP (delegates to running processes)
        └── Knowledge MCP (delegates to knowledge bases)
```

### 2.2 LCT Structure for MCP Servers

Each MCP server has an LCT with specific fields:

```json
{
  "lct_version": "1.0",
  "entity_id": "mcp:database_server_001",
  "entity_type": "mcp_server",
  "entity_subtype": "database",
  "capabilities": {
    "responsive": true,
    "delegative": true,
    "agentic": false
  },
  "mcp_metadata": {
    "protocol_version": "1.0",
    "supported_methods": ["query", "insert", "update", "delete"],
    "backend_type": "postgresql",
    "connection_pool_size": 10
  },
  "mrh": {
    "@graph": [
      {
        "@type": "mrh:Relevance",
        "mrh:target": {"@id": "lct:database_schema"},
        "mrh:probability": 0.95,
        "mrh:relation": "mrh:depends_on"
      },
      {
        "@type": "mrh:Relevance",
        "mrh:target": {"@id": "lct:auth_service"},
        "mrh:probability": 0.8,
        "mrh:relation": "mrh:delegates_to"
      }
    ]
  },
  "trust_tensor": {
    "response_accuracy": 0.98,
    "delegation_reliability": 0.95,
    "latency_consistency": 0.87
  }
}
```

## 3. MCP in the Web4 Equation

### 3.1 MCP's Role in the Equation

The canonical Web4 equation:

```
Web4 = MCP + RDF + LCT + T3/V3*MRH + ATP/ADP
```

MCP is the **I/O membrane** — the sensory interface through which entities interact. RDF provides the ontological backbone, LCTs provide presence, T3/V3*MRH provides role-contextualized trust, and ATP/ADP provides energy metabolism. MCP servers are entities in the RDF graph, and their relationships are typed triples.

### 3.2 Why MCP Completes Web4

MCP servers are essential because they:

1. **Bridge the Gap**: Connect AI models to real-world data and tools
2. **Maintain Abstraction**: Hide complexity while preserving capability
3. **Enable Composition**: Allow complex workflows through server chaining
4. **Preserve Context**: Maintain MRH across tool invocations

## 4. MCP Server Types and Examples

### 4.1 Database MCP Server

```python
class DatabaseMCPServer(MCPEntity):
    """MCP server delegating to database backends"""
    
    def __init__(self, backend_url: str):
        super().__init__(entity_type="mcp_server", subtype="database")
        self.backend = DatabaseConnection(backend_url)
        self.capabilities = {
            "responsive": True,  # Returns query results
            "delegative": True,  # Delegates to actual database
            "methods": ["query", "insert", "update", "delete"]
        }
    
    async def handle_request(self, method: str, params: Dict) -> Dict:
        """Responsive: process request and return result"""
        if method == "query":
            # Delegative: forward to backend
            result = await self.backend.execute(params["sql"])
            # Add to MRH
            self.mrh.add_relevance(
                target=f"lct:query_{hash(params['sql'])}",
                probability=0.9,
                relation="mrh:produces"
            )
            return {"status": "success", "data": result}
```

### 4.2 Tool MCP Server

```python
class ToolMCPServer(MCPEntity):
    """MCP server delegating to CLI tools"""
    
    def __init__(self, tool_path: str):
        super().__init__(entity_type="mcp_server", subtype="tool")
        self.tool = ToolWrapper(tool_path)
        self.capabilities = {
            "responsive": True,  # Returns tool output
            "delegative": True,  # Delegates to external tool
            "methods": ["execute", "status", "cancel"]
        }
    
    async def handle_request(self, method: str, params: Dict) -> Dict:
        """Bridge between AI and external tools"""
        if method == "execute":
            # Delegative: run external tool
            process = await self.tool.run(params["command"], params["args"])
            # Track in MRH
            self.mrh.add_relevance(
                target=f"lct:execution_{process.id}",
                probability=1.0,
                relation="mrh:produces"
            )
            return {"status": "running", "pid": process.id}
```

### 4.3 Knowledge MCP Server

```python
class KnowledgeMCPServer(MCPEntity):
    """MCP server delegating to knowledge bases"""
    
    def __init__(self, kb_endpoint: str):
        super().__init__(entity_type="mcp_server", subtype="knowledge")
        self.knowledge_base = KnowledgeBaseClient(kb_endpoint)
        self.capabilities = {
            "responsive": True,  # Returns knowledge
            "delegative": True,  # Delegates to KB system
            "methods": ["search", "retrieve", "update_graph"]
        }
    
    async def handle_request(self, method: str, params: Dict) -> Dict:
        """Semantic bridge to knowledge systems"""
        if method == "search":
            # Delegative: query knowledge base
            results = await self.knowledge_base.semantic_search(
                query=params["query"],
                limit=params.get("limit", 10)
            )
            # Update MRH with discovered contexts
            for result in results:
                self.mrh.add_relevance(
                    target=result.lct_id,
                    probability=result.relevance_score,
                    relation="mrh:references"
                )
            return {"status": "success", "results": results}
```

## 5. Trust and Reputation for MCP Servers

### 5.1 Trust Metrics

MCP servers track trust across multiple dimensions:

1. **Response Trust**: Accuracy and completeness of responses
2. **Delegation Trust**: Reliability of backend delegation
3. **Latency Trust**: Consistency of response times
4. **Security Trust**: Proper authentication and authorization

### 5.2 Trust Computation

```python
def compute_mcp_trust(server: MCPServer) -> float:
    """Compute overall trust score for MCP server"""
    
    # Base trust from successful operations
    success_rate = server.successful_ops / server.total_ops
    
    # Delegation-specific trust
    delegation_reliability = server.successful_delegations / server.total_delegations
    
    # Latency consistency (lower variance = higher trust)
    latency_trust = 1.0 / (1.0 + server.latency_variance)
    
    # Security trust (failed auth attempts reduce trust)
    security_trust = 1.0 - (server.failed_auth / server.total_auth)
    
    # Weighted combination
    trust = (
        0.3 * success_rate +
        0.3 * delegation_reliability +
        0.2 * latency_trust +
        0.2 * security_trust
    )
    
    return trust
```

## 6. MCP Servers in SAGE Architecture

### 6.1 Integration with H-Level and L-Level

MCP servers integrate with SAGE's dual architecture:

#### H-Level (Strategic) Interaction
- H-level determines which MCP servers to use
- Evaluates trust scores for server selection
- Plans multi-server workflows
- Monitors overall delegation success

#### L-Level (Tactical) Interaction
- L-level executes specific MCP requests
- Handles low-level protocol details
- Manages connection pooling
- Implements retry logic

### 6.2 Example SAGE-MCP Workflow

```python
class SAGEWithMCP:
    """SAGE system with MCP server integration"""
    
    def __init__(self):
        self.h_level = HLevelReasoner()
        self.l_level = LLevelExecutor()
        self.mcp_registry = MCPRegistry()
    
    async def process_task(self, task: str):
        # H-level: Determine required MCP servers
        required_servers = self.h_level.identify_resources(task)
        
        # Select servers based on trust
        selected_servers = []
        for requirement in required_servers:
            candidates = self.mcp_registry.find_servers(requirement)
            best_server = max(candidates, key=lambda s: s.trust_score)
            selected_servers.append(best_server)
        
        # Create execution plan
        plan = self.h_level.create_plan(task, selected_servers)
        
        # L-level: Execute plan using MCP servers
        results = []
        for step in plan:
            if step.requires_mcp:
                # Delegate to MCP server
                result = await self.l_level.call_mcp(
                    server=step.mcp_server,
                    method=step.method,
                    params=step.params
                )
                # Update server trust based on result
                step.mcp_server.update_trust(result.success)
            else:
                # Direct execution
                result = await self.l_level.execute(step)
            
            results.append(result)
        
        return results
```

## 7. MCP Protocol Extensions for Web4

### 7.1 Web4-Specific Fields

MCP messages in Web4 include additional fields:

```json
{
  "jsonrpc": "2.0",
  "method": "query",
  "params": {
    "sql": "SELECT * FROM users WHERE active = true"
  },
  "web4_context": {
    "requesting_lct": "lct:ai_agent_001",
    "mrh_depth": 2,
    "trust_requirement": 0.8,
    "delegation_chain": ["lct:orchestrator", "mcp:database_server"]
  }
}
```

### 7.2 Trust Attestation

MCP servers provide trust attestations:

```json
{
  "jsonrpc": "2.0",
  "result": {
    "data": [...],
    "trust_attestation": {
      "server_lct": "mcp:database_server_001",
      "confidence": 0.95,
      "delegation_target": "postgresql://db.internal",
      "latency_ms": 45,
      "cache_hit": false
    }
  }
}
```

## 8. Benefits of MCP as Web4 Entity

### 8.1 Unified Resource Access
- Single protocol for all external resources
- Consistent trust and identity model
- Standardized error handling

### 8.2 Composability
- MCP servers can chain delegations
- Complex workflows through server composition
- Fractal architecture support

### 8.3 Trust-Based Selection
- Choose servers based on trust scores
- Automatic failover to trusted alternatives
- Reputation-based load balancing

### 8.4 Context Preservation
- MRH maintained across delegations
- Full provenance tracking
- Audit trail for all operations

## 9. Implementation Roadmap

### Phase 1: Basic MCP Entity Support
- [ ] Define MCP entity type in Web4 ontology
- [ ] Implement basic LCT structure for MCP servers
- [ ] Create trust metrics for MCP operations

### Phase 2: SAGE Integration
- [ ] Integrate MCP servers with H-level planning
- [ ] Implement L-level MCP protocol handling
- [ ] Build MCP server registry

### Phase 3: Advanced Features
- [ ] Multi-server delegation chains
- [ ] Trust-based server selection
- [ ] MRH propagation across MCP calls
- [ ] Distributed MCP server discovery

## 10. Conclusion

MCP servers as Web4 entities complete the equation for distributed intelligence by providing the crucial bridge between AI models and external resources. Their dual nature as both responsive and delegative entities makes them unique in the Web4 taxonomy, enabling complex workflows while maintaining the trust and context preservation that Web4 demands.

By treating MCP servers as first-class entities with their own LCTs, MRH, and trust metrics, Web4 creates a unified framework where AI agents can reliably interact with the full spectrum of digital resources, from databases to APIs to running processes, all while maintaining cryptographic presence, contextual relevance, and trust-based coordination.

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*"MCP servers are the synapses of distributed intelligence—responsive to requests, delegative to resources, and trusted through experience."* - Dennis Palatov