Add MCP integration for stock price retrieval

- Introduced multiple new files for MCP agent functionality, including `mcp-agents-detailed.py`, `mcp-agents.py`, `mcp-basic.py`, `mcp-multiagents.py`, and `mcp-test.py`.
- Implemented a dedicated `MCP` class for managing connections and tool execution.
- Created an asynchronous function to retrieve stock prices using MCP tools.
- Added example agents for stock price checking and web searching.
- Updated `pyproject.toml` and `uv.lock` to reflect version increment to 0.0.66.
- Ensured backward compatibility and improved tool function generation for better usability.

Author: MervinPraison

src/praisonai-agents/mcp-agents-detailed.py

@@ -0,0 +1,13 @@
+from praisonaiagents import Agent, MCP
+
+agent = Agent(
+    instructions="""You are a helpful assistant that can check stock prices and perform other tasks.
+    Use the available tools when relevant to answer user questions.""",
+    llm="gpt-4o-mini",
+    tools=MCP(
+        command="/Users/praison/miniconda3/envs/mcp/bin/python",
+        args=["/Users/praison/stockprice/app.py"]
+    )
+)
+
+agent.start("What is the stock price of Tesla?")

src/praisonai-agents/mcp-agents.py

@@ -0,0 +1,10 @@
+from praisonaiagents import Agent, MCP
+
+agent = Agent(
+    instructions="""You are a helpful assistant that can check stock prices and perform other tasks.
+    Use the available tools when relevant to answer user questions.""",
+    llm="gpt-4o-mini",
+    tools = MCP("/Users/praison/miniconda3/envs/mcp/bin/python /Users/praison/stockprice/app.py")
+)
+
+agent.start("What is the stock price of Tesla?")

src/praisonai-agents/mcp-basic.py

@@ -0,0 +1,61 @@
+import asyncio
+from praisonaiagents import Agent
+from mcp import ClientSession, StdioServerParameters
+from mcp.client.stdio import stdio_client
+
+# Define server configuration - pointing to your stock price app
+server_params = StdioServerParameters(
+    command="/Users/praison/miniconda3/envs/mcp/bin/python",
+    args=[
+        "/Users/praison/stockprice/app.py",
+    ],
+)
+
+# Function to get stock price using MCP
+async def get_stock_price(symbol):
+    # Start server and connect client
+    async with stdio_client(server_params) as (read, write):
+        async with ClientSession(read, write) as session:
+            # Initialize the connection
+            await session.initialize()
+            
+            # Get tools
+            tools_result = await session.list_tools()
+            tools = tools_result.tools
+            print(f"Available tools: {[tool.name for tool in tools]}")
+            
+            # Find a tool that can get stock prices
+            # Assuming there's a tool like "get_stock_price" or similar
+            stock_tool = None
+            for tool in tools:
+                if "stock" in tool.name.lower() or "price" in tool.name.lower():
+                    stock_tool = tool
+                    break
+            
+            if stock_tool:
+                print(f"Using tool: {stock_tool.name}")
+                # Call the tool with the stock symbol
+                result = await session.call_tool(
+                    stock_tool.name, 
+                    arguments={"ticker": symbol}
+                )
+                return result
+            else:
+                return "No suitable stock price tool found"
+
+# Create a custom tool for the agent
+def stock_price_tool(symbol: str) -> str:
+    """Get the current stock price for a given symbol"""
+    # Run the async function to get the stock price
+    result = asyncio.run(get_stock_price(symbol))
+    return f"Stock price for {symbol}: {result}"
+
+# Create agent with the stock price tool
+agent = Agent(
+    instructions="You are a helpful assistant that can check stock prices. When asked about stock prices, use the stock_price_tool.",
+    llm="gpt-4o-mini",
+    tools=[stock_price_tool]
+)
+
+# Start the agent
+agent.start("What is the stock price of Tesla?")

src/praisonai-agents/mcp-multiagents.py

@@ -0,0 +1,21 @@
+from praisonaiagents import Agent, MCP
+
+stock_agent = Agent(
+    instructions="""You are a helpful assistant that can check stock prices and perform other tasks.
+    Use the available tools when relevant to answer user questions.""",
+    llm="gpt-4o-mini",
+    tools=MCP(
+        command="/Users/praison/miniconda3/envs/mcp/bin/python",
+        args=["/Users/praison/stockprice/app.py"]
+    )
+)
+
+search_agent = Agent(
+    instructions="""You are a helpful assistant that can search the web for information.
+    Use the available tools when relevant to answer user questions.""",
+    llm="gpt-4o-mini",
+    tools=MCP('npx -y @smithery/cli@latest install @smithery-ai/brave-search --client claude --config "{\"braveApiKey\":\"BSANfDaqLKO9wq7e08mrPth9ZlJvKtc\"}"')
+)
+
+stock_agent.start("What is the stock price of Tesla?")
+search_agent.start("What is the weather in San Francisco?")

src/praisonai-agents/mcp-test.py

@@ -0,0 +1,69 @@
+import asyncio
+import json
+from typing import Dict, Any
+from mcp import ClientSession, StdioServerParameters
+from mcp.client.stdio import stdio_client
+
+# Define server configuration
+server_params = StdioServerParameters(
+      command="/Users/praison/miniconda3/envs/mcp/bin/python",
+      args=[
+          "/Users/praison/stockprice/app.py",
+      ],
+  )
+
+async def execute_tool(session: ClientSession, tool_name: str, params: Dict[str, Any]) -> Any:
+    """
+    Execute a tool with proper error handling and return the result.
+    
+    This follows the pattern shown in the article for reliable tool execution.
+    """
+    try:
+        result = await session.call_tool(tool_name, arguments=params)
+        return result
+    except Exception as e:
+        print(f"Error executing tool {tool_name}: {str(e)}")
+        return {"error": str(e)}
+
+async def main():
+    # Start server and connect client
+    async with stdio_client(server_params) as (read, write):
+        async with ClientSession(read, write) as session:
+            # Initialize the connection
+            await session.initialize()
+            
+            # Get details of available tools
+            tools_result = await session.list_tools()
+            tools = tools_result.tools
+            
+            # Print available tools for debugging
+            print(f"Available tools: {[tool.name for tool in tools]}")
+            
+            # Example: Call a tool if it exists
+            if tools and len(tools) > 0:
+                # Assuming first tool as an example
+                tool = tools[0]
+                print(f"Calling tool: {tool.name}")
+                print(f"Tool schema: {json.dumps(tool.inputSchema, indent=2)}")
+                
+                # Create parameters based on the tool's input schema
+                # This is a simplification - in a real application, you would parse
+                # the schema and provide appropriate values
+                params = {}
+                
+                # For demonstration, we'll check if the tool needs any parameters
+                if tool.inputSchema and "properties" in tool.inputSchema:
+                    # Just populate with empty values for demonstration
+                    params = {
+                        key: "" for key in tool.inputSchema["properties"].keys()
+                    }
+                
+                # Call the tool with appropriate parameters
+                response = await execute_tool(session, tool.name, params)
+                
+                # Process the response
+                print(f"Tool response: {response}")
+
+# Run the async function
+if __name__ == "__main__":
+    asyncio.run(main())

src/praisonai-agents/praisonaiagents/__init__.py

@@ -10,6 +10,7 @@
 from .agents.autoagents import AutoAgents
 from .knowledge.knowledge import Knowledge
 from .knowledge.chunking import Chunking
+from .mcp.mcp import MCP
 from .main import (
     TaskOutput,
     ReflectionOutput,
@@ -51,5 +52,6 @@
     'sync_display_callbacks',
     'async_display_callbacks',
     'Knowledge',
-    'Chunking'
+    'Chunking',
+    'MCP'
 ]