release 0.1.9 - HotFix and add Example

examples/with_autogen.py

@@ -0,0 +1,45 @@
+"""
+Example of using MCPHub with Autogen Agents.
+1. Initialize MCPHub to manage MCP servers
+2. Fetch MCP tools and adapters for Autogen
+3. Create and run an agent with MCP tools
+"""
+
+import asyncio
+
+from autogen_agentchat.agents import AssistantAgent
+from autogen_agentchat.ui import Console
+from autogen_core import CancellationToken
+from autogen_ext.models.openai import OpenAIChatCompletionClient
+from mcphub import MCPHub
+
+
+async def main():
+    # Initialize MCPHub - automatically loads .mcphub.json and sets up servers
+    hub = MCPHub()
+
+    # Fetch MCP tools adapted for Autogen
+    tool_adapters = await hub.fetch_autogen_mcp_adapters("azure-storage-mcp")
+    model_client = OpenAIChatCompletionClient(model="gpt-4")
+
+    # Create and run agent with MCP tools
+    complex_task = """Please help me analyze the following complex problem: 
+                We need to design a new feature for our product that balances user privacy 
+                with data collection for improving the service. Consider the ethical implications, 
+                technical feasibility, and business impact. Break down your thinking process 
+                step by step, and provide a detailed recommendation with clear justification 
+                for each decision point."""
+    agent = AssistantAgent(
+        name="assistant",
+        model_client=model_client,
+        tools=tool_adapters,
+        system_message="You are a helpful assistant.",
+    )
+
+    await Console(
+        agent.run_stream(task=complex_task, cancellation_token=CancellationToken())
+    )
+
+if __name__ == "__main__":
+    # Run the async main function
+    asyncio.run(main())

examples/with_langchain.py

@@ -0,0 +1,45 @@
+"""
+Example of using MCPHub with LangChain Agents.
+1. Initialize MCPHub to manage MCP servers
+2. Fetch MCP tools for LangChain
+3. Create and run an agent with MCP tools
+"""
+
+import asyncio
+import json
+
+from langchain_mcp_adapters.tools import load_mcp_tools
+from langchain_openai import ChatOpenAI
+from langgraph.prebuilt import create_react_agent
+from mcp import ClientSession, StdioServerParameters
+from mcp.client.stdio import stdio_client
+from mcphub import MCPHub
+
+model = ChatOpenAI(model="gpt-4o")
+
+async def main():
+    # Initialize MCPHub - automatically loads .mcphub.json and sets up servers
+    hub = MCPHub()
+
+    # Fetch MCP tools for LangChain
+    tools = await hub.fetch_langchain_mcp_tools("azure-storage-mcp")
+    tools_dict = [
+        {"name": tool.name, "description": tool.description, "args_schema": tool.args_schema} for tool in tools
+    ]
+    print("Available MCP Tools:")
+    print(json.dumps(tools_dict, indent=2))
+
+    # Create and run agent with MCP tools
+    complex_task = """Please help me analyze the following complex problem: 
+                We need to design a new feature for our product that balances user privacy 
+                with data collection for improving the service. Consider the ethical implications, 
+                technical feasibility, and business impact. Break down your thinking process 
+                step by step, and provide a detailed recommendation with clear justification 
+                for each decision point."""
+    agent = create_react_agent(model, tools)
+    agent_response = await agent.ainvoke({"messages": complex_task})
+    print("\nAgent Response:")
+    print(agent_response.get("messages")[1].content)
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/test.py → examples/with_openai.py

@@ -1,67 +1,51 @@
+"""
+Example of using MCPHub with OpenAI Agents.
+1. Initialize MCPHub to manage MCP servers
+2. Fetch an MCP server with async context manager
+3. List available tools from the server
+4. Create and run an agent with MCP tools
+"""
+
 import asyncio
 import json
 from agents import Agent, Runner
 from mcphub import MCPHub
 
 async def main():
-    """
-    Example of using MCPHub to integrate MCP servers with OpenAI Agents.
-
-    This example demonstrates:
-    1. Initializing MCPHub
-    2. Fetching and using an MCP server
-    3. Listing available tools
-    4. Creating and running an agent with MCP tools
-    """
-
-    # Step 1: Initialize MCPHub
-    # MCPHub will automatically:
-    # - Find .mcphub.json in your project
-    # - Load server configurations
-    # - Set up servers (clone repos, run setup scripts if needed)
+    # Initialize MCPHub - automatically loads .mcphub.json and sets up servers
     hub = MCPHub()
 
-    # Step 2: Create an MCP server instance using async context manager
-    # Parameters:
-    # - mcp_name: The name of the server from your .mcphub.json
-    # - cache_tools_list: Cache the tools list for better performance
+    # Fetch MCP server - handles server setup and tool caching
     async with hub.fetch_openai_mcp_server(
         mcp_name="sequential-thinking-mcp",
         cache_tools_list=True
     ) as server:
-        # Step 3: List available tools from the MCP server
-        # This shows what capabilities are available to your agent
+        # Get available tools from the server
         tools = await server.list_tools()
-
-        # Pretty print the tools for better readability
         tools_dict = [
             dict(tool) if hasattr(tool, "__dict__") else tool for tool in tools
         ]
         print("Available MCP Tools:")
         print(json.dumps(tools_dict, indent=2))
 
-        # Step 4: Create an OpenAI Agent with MCP server
-        # The agent can now use all tools provided by the MCP server
+        # Create agent with MCP server integration
         agent = Agent(
             name="Assistant",
             instructions="Use the available tools to accomplish the given task",
-            mcp_servers=[server]  # Provide the MCP server to the agent
+            mcp_servers=[server]
         )
 
-        # Step 5: Run your agent with a complex task
-        # The agent will automatically have access to all MCP tools
+        # Run agent with a task
         complex_task = """Please help me analyze the following complex problem: 
                       We need to design a new feature for our product that balances user privacy 
                       with data collection for improving the service. Consider the ethical implications, 
                       technical feasibility, and business impact. Break down your thinking process 
                       step by step, and provide a detailed recommendation with clear justification 
                       for each decision point."""
 
-        # Execute the task and get the result
         result = await Runner.run(agent, complex_task)
         print("\nAgent Response:")
         print(result)
 
 if __name__ == "__main__":
-    # Run the async main function
     asyncio.run(main())

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "mcphub"
-version = "0.1.8"
+version = "0.1.9"
 description = "A Python package for managing and integrating Model Context Protocol (MCP) servers with AI frameworks like OpenAI Agents, LangChain, and Autogen"
 readme = "README.md"
 authors = [

src/mcphub/adapters/autogen.py

@@ -1,17 +1,25 @@
 try:
     from typing import List
 
-    from autogen_ext.tools.mcp import StdioMcpToolAdapter
+    from autogen_ext.tools.mcp import StdioMcpToolAdapter, StdioServerParams
 
     from .base import MCPBaseAdapter
 
     class MCPAutogenAdapter(MCPBaseAdapter):
         async def create_adapters(self, mcp_name: str) -> List[StdioMcpToolAdapter]:
             server_params = self.get_server_params(mcp_name)
+
+            autogen_mcp_server_params = StdioServerParams(
+                command=server_params.command,
+                args=server_params.args,
+                env=server_params.env,
+                cwd=server_params.cwd
+            )
+
             async with self.create_session(mcp_name) as session:
                 tools = await session.list_tools()
                 return [
-                    await StdioMcpToolAdapter.from_server_params(server_params, tool.name)
+                    await StdioMcpToolAdapter.from_server_params(autogen_mcp_server_params, tool.name)
                     for tool in tools.tools
                 ]