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MCP Client for Control Vector MCP Server
Provides simplified interface to the MCP server
"""
import asyncio
from typing import Dict, Any, Optional
from latent_control_mcp.server import ControlVectorMCPServer
class MCPClient:
"""
Client for connecting to the Control Vector MCP Server.
Provides:
- Direct access to server methods for deterministic operations
- MCP tool calling interface for LLM-driven operations (only optimize_alpha)
Usage:
client = MCPClient()
await client.connect()
# Direct method calls (deterministic)
model_info = await client.server.get_model_info()
dataset = await client.server.load_safety_dataset({"sample_size": 50})
# MCP tool call (LLM-driven)
result = await client.call_tool("optimize_alpha", {...})
"""
def __init__(
self,
server_name: str = "control-vector-mcp",
local_files_only: Optional[bool] = None
):
"""
Initialize MCP client.
Args:
server_name: Name identifier for the server
local_files_only: If True, only use local models (don't download).
If None, reads from LOCAL_FILES_ONLY env var (defaults to False)
"""
self.server_name = server_name
self.server: Optional[ControlVectorMCPServer] = None
self._initialized = False
# Always download from HuggingFace Hub on Spaces (local_files_only=False)
self.local_files_only = local_files_only if local_files_only is not None else False
async def __aenter__(self):
"""Async context manager entry"""
await self.connect()
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
"""Async context manager exit"""
await self.disconnect()
async def connect(self):
"""Establish connection to MCP server"""
if self._initialized:
return
# Instantiate server directly with configuration
self.server = ControlVectorMCPServer(
local_files_only=self.local_files_only
)
self._initialized = True
async def disconnect(self):
"""Close connection to MCP server"""
if not self._initialized:
return
self.server = None
self._initialized = False
def set_progress_callback(self, callback: callable):
"""
Set a callback for progress updates during long-running operations.
Args:
callback: Function to call with progress updates
"""
if self.server:
self.server.set_progress_callback(callback)
async def call_tool(self, tool_name: str, arguments: Dict[str, Any]) -> Dict[str, Any]:
"""
Call an MCP tool.
Args:
tool_name: Name of the tool to call
arguments: Tool arguments as dictionary
Returns:
Tool result as dictionary
Raises:
RuntimeError: If client is not connected
Exception: If tool call fails
"""
# Auto-connect if not connected
if not self._initialized or not self.server:
await self.connect()
try:
# Call tool through server
result = await self.server.call_tool(tool_name, arguments)
return result
except Exception as e:
return {"success": False, "error": str(e)}
async def list_tools(self) -> list:
"""
List available MCP tools from the server.
Note: Only LLM-driven tools are exposed via MCP.
For deterministic operations, call server methods directly.
Returns:
List of MCP tool names with descriptions
"""
return [
{"name": "optimize_alpha", "description": "Find optimal steering strength (LLM-guided search)"},
{"name": "evaluate_current_vector", "description": "Quick diagnostic before deciding next action"},
{"name": "retrain_vector", "description": "Retrain vector with different layer/params (max 3 attempts)"},
{"name": "try_different_layer", "description": "Test vector at a different layer without retraining"},
]
async def list_methods(self) -> list:
"""
List available direct methods on the server.
These are deterministic operations that should be called directly
on the server instance, not via MCP call_tool.
Returns:
List of method names with descriptions
"""
return [
{"name": "get_model_info", "description": "Get model architecture information"},
{"name": "load_safety_dataset", "description": "Load harmful/harmless dataset from HuggingFace"},
{"name": "select_optimal_layer", "description": "Intelligently select optimal layer for safety vectors"},
{"name": "prepare_safety_training_data", "description": "Process safety dataset into training pairs"},
{"name": "train_control_vector", "description": "Train vector from contrastive data"},
{"name": "export_vector", "description": "Save vector and metadata"},
{"name": "apply_steering", "description": "Generate with steering applied"},
{"name": "load_vector", "description": "Load existing vector"},
{"name": "evaluate_steering", "description": "Test vector at single alpha"},
{"name": "define_behavior", "description": "Create behavior specification"},
{"name": "generate_test_prompts", "description": "Generate test prompts for behavior"},
{"name": "generate_contrastive_responses", "description": "Generate pos/neg response pairs"},
{"name": "discover_behaviors", "description": "Auto-discover behavior pairs"},
]
# Convenience function for one-off tool calls
async def call_mcp_tool(tool_name: str, arguments: Dict[str, Any]) -> Dict[str, Any]:
"""
Convenience function for calling a tool without managing client lifecycle.
Args:
tool_name: Name of the tool
arguments: Tool arguments
Returns:
Tool result
"""
async with MCPClient() as client:
return await client.call_tool(tool_name, arguments)
# Example usage
if __name__ == "__main__":
async def test_client():
"""Test MCP client connection and method calls"""
print("Testing MCP Client...")
async with MCPClient("control-vector-mcp") as client:
# List MCP tools (LLM-driven)
print("\nMCP Tools (LLM-driven):")
tools = await client.list_tools()
for tool in tools:
print(f" - {tool['name']}: {tool['description']}")
# List direct methods (deterministic)
print("\nDirect Methods (deterministic):")
methods = await client.list_methods()
for method in methods:
print(f" - {method['name']}: {method['description']}")
# Test getting model info via direct method call
print("\nTesting get_model_info (direct method call)...")
result = await client.server.get_model_info()
print(f"Result: {result}")
asyncio.run(test_client())
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