MCP client with ollama.py

import asyncio
import json
from typing import Optional
from contextlib import AsyncExitStack

from dotenv import load_dotenv
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
import ollama

load_dotenv()


class MCPClient:
    def __init__(self):
        self.session: Optional[ClientSession] = None
        self.exit_stack = AsyncExitStack()
        self.ollama_client = ollama.Client(host="http://localhost:11434")

    @staticmethod
    def convert_tools_format(tools) -> dict:
        """
        Convert the MCP list_tools response to Ollama tool use format.
        
        Args:
            tools: MCP server tools
        """
        converted = []

        for tool in tools:
            converted_tool = {
                "type": "function",
                "function": {
                    "name": tool.name,
                    "description": tool.description,
                    "parameters": tool.inputSchema,
                },
            }

            converted.append(converted_tool)

        return converted

    async def connect_to_server(self, server_script_path: str):
        """Connect to an MCP server

        Args:
            server_script_path: Path to the server script (.py or .js)
        """
        is_python = server_script_path.endswith(".py")
        is_js = server_script_path.endswith(".js")
        if not (is_python or is_js):
            raise ValueError("Server script must be a .py or .js file")

        command = "python" if is_python else "node"
        server_params = StdioServerParameters(
            command=command, args=[server_script_path], env=None
        )

        stdio_transport = await self.exit_stack.enter_async_context(
            stdio_client(server_params)
        )
        self.stdio, self.write = stdio_transport
        self.session = await self.exit_stack.enter_async_context(
            ClientSession(self.stdio, self.write)
        )

        await self.session.initialize()

        response = await self.session.list_tools()
        tools = response.tools
        print("\nConnected to server with tools:", [tool.name for tool in tools])

    async def process_query(self, query: str, model: str = "gpt-oss:20b") -> str:
        """
        Send `user_prompt` to the local Ollama model.
        If the model asks to use a tool, call the Python function and feed
        the result back as a "tool" message.  Return the final answer.
        """
        # Start conversation with a user message
        messages = [{"role": "user", "content": query}]

        mcp_tools = await self.session.list_tools()
        available_tools = MCPClient.convert_tools_format(mcp_tools.tools)
        print(f"|   Available tools: {available_tools}")
        while True:
            # Ask the model (no streaming – we want a single message back)
            print(f"|   |   Message to model: {messages}")
            response = self.ollama_client.chat(
                model=model,
                messages=messages,
                tools=available_tools,
                stream=False,
            )
            
            print(f"|   |   Model response: {response}")
            tool_calls = response.message.get("tool_calls") or []

            if tool_calls:
                print(f"|   |   |   Tool calls: {tool_calls}")
                # For simplicity we assume a single tool call per round
                call = tool_calls[0].function
                tool_name = call.get("name")
                tool_args = call.get("arguments", "{}")

                try:
                    tool_args = json.loads(tool_args)
                except json.JSONDecodeError:
                    print(f"JSON LOAD ERROR with: {tool_args}")
                    tool_args = {}
                except TypeError:
                    pass
                except Exception as e:
                    raise e

                # Dispatch to the Python function
                result = await self.session.call_tool(tool_name, tool_args)
                print(f"""|   |   |   Tool response: {result}""")
                messages.append(
                    {"role": "tool", "name": tool_name, "content": str(result)}
                )

            else:
                # The model has produced a final answer
                print(f"Final response: {response}")
                return response.get("message", {}).get("content", "")

    async def chat_loop(self):
        """Run an interactive chat loop"""
        print("MCP Client Started!")
        print("Type your queries or 'quit' to exit.")

        while True:
            try:
                query = input("\nQuery: ").strip()

                if query.lower() == "quit":
                    break

                response = await self.process_query(query)
                print("\n" + response)

            except Exception as e:
                print(f"\nError: {str(e)}")

    async def cleanup(self):
        """Clean up resources"""
        await self.exit_stack.aclose()


async def main():
    if len(sys.argv) < 2:
        print("Usage: python client.py <path_to_server_script>")
        sys.exit(1)

    client = MCPClient()
    try:
        await client.connect_to_server(sys.argv[1])
        await client.chat_loop()
    finally:
        await client.cleanup()


if __name__ == "__main__":
    import sys

    asyncio.run(main())