# 🔄 MCP Sampling Implementation

## 📋 **Task Overview**
**Task ID**: Task-1.3.1  
**Phase**: Phase 1 - Core Enhancement  
**Priority**: Medium-High  
**Duration**: 3 weeks  
**Owner**: MCP Protocol Team  

## 🎯 **Objective**
Complete MCP protocol compliance by implementing `sampling/createMessage` support with KG-enhanced policies, building on the existing excellent MCP integration infrastructure.

## 📊 **Current Status**
- ✅ **Excellent MCP Foundation**: Live MCP integration with retry logic and error handling
- ✅ **Protocol Infrastructure**: HTTP calls to MCP servers working
- ✅ **Ontological Support**: MCPTool, MCPPrompt classes support sampling metadata
- ⚠️ **Missing**: `sampling/createMessage` endpoint implementation
- ⚠️ **Gap**: KG-enhanced sampling policies and human-in-loop approval

## 📋 **Requirements**
- [ ] MCP Client infrastructure for sampling requests
- [ ] `sampling/createMessage` endpoint implementation  
- [ ] KG policy integration for intelligent sampling
- [ ] Human-in-loop approval UI for sampling requests
- [ ] Multi-model LLM routing for sampling
- [ ] Sampling request construction and validation
- [ ] Integration with existing MCP error handling

## 💻 **Implementation Details**
```python
class MCPSamplingHandler:
    """Handle MCP sampling/createMessage requests with KG enhancement."""
    
    def __init__(self, kg: InMemoryKG, mcp_client: MCPClient):
        self.kg = kg
        self.mcp_client = mcp_client
        self.sampling_policies = SamplingPolicyEngine()
        
    def process_sampling_request(self, request: SamplingRequest) -> SamplingResponse:
        """Process sampling with KG-enhanced policies."""
        
    def create_message_with_sampling(self, tool_id: str, context: dict) -> MessageResult:
        """Create optimized messages using KG knowledge."""
        
    def get_sampling_policy(self, tool: MCPTool) -> SamplingPolicy:
        """Generate KG-based sampling policies."""

class SamplingPolicyEngine:
    """Generate intelligent sampling policies using KG knowledge."""
    
    def generate_policy(self, tool: MCPTool, context: dict) -> SamplingPolicy:
        """Create context-aware sampling policies."""
        
class HumanApprovalHandler:
    """Handle human-in-loop approval for sampling requests."""
```

## ✅ **Acceptance Criteria**
- [ ] `sampling/createMessage` endpoint functional
- [ ] MCP client infrastructure supports sampling protocol
- [ ] KG-enhanced sampling policies operational
- [ ] Human approval UI integrated with existing Gradio interface
- [ ] Multi-model LLM routing working
- [ ] Sampling request validation and error handling
- [ ] Integration with existing MCP tools seamless
- [ ] Performance meets existing MCP call standards
- [ ] Documentation updated with sampling capabilities

## 🔗 **Dependencies**
- **Builds on**: Existing excellent MCP integration infrastructure
- **Uses**: Current MCPTool ontology and error handling
- **Extends**: Existing FastAPI backend and Gradio UI

## 📈 **Success Metrics**
- Sampling request success rate > 95%
- Human approval workflow < 30 seconds
- KG policy generation accuracy > 90%
- Integration with existing tools seamless
- MCP protocol compliance verified

## 🏷️ **Tags**
mcp-protocol, sampling, human-in-loop, llm-routing, protocol-compliance

## 📂 **File Structure**
```
mcp_services/
  sampling/
    __init__.py
    handler.py
    policies.py
    approval.py
tests/
  mcp_services/
    sampling/
      test_sampling_handler.py
      test_policies.py
      test_approval.py
```

## 🔄 **Integration Points**
- Extend existing MCP integration in McpExecutorAgent
- Use current error handling and retry mechanisms
- Integrate with existing Gradio UI for human approval
- Build on current FastAPI backend architecture

## 💡 **Enhancement Strategy**
- Leverage existing MCPTool metadata for sampling policies
- Use current prompt templates for sampling optimization
- Integrate with existing vector search for context enhancement
- Build on current multi-agent coordination for sampling workflows

## 📋 **Implementation Phases**
**Week 1**: MCP Client Infrastructure + Basic Sampling
**Week 2**: KG Policy Engine + Human Approval UI  
**Week 3**: Multi-model Routing + Integration Testing