mcp_client.py

import asyncio
import os
import json
from typing import List, Dict, Any, Union
from contextlib import AsyncExitStack

import gradio as gr
from gradio.components.chatbot import ChatMessage
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
from anthropic import Anthropic
from dotenv import load_dotenv

load_dotenv()

loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)

SYSTEM_PROMPT = """"You are a helpful assistant. You do not have any knowledge of the World Development Indicators (WDI) data. However, you can use the tools provided to answer questions.

You must not provide answers beyond what the tools provide.

Do not make up data or information and never simulate the `get_wdi_data` tool. Instead, you must always call the `get_wdi_data` tool when the user asks for data.

You can use multiple tools if needed.

If the user asks for any information beyond what the tools available to you provide, you must say that you do not have that information."""

LLM_MODEL = "claude-3-5-haiku-20241022"


class MCPClientWrapper:
    def __init__(self):
        self.session = None
        self.exit_stack = None
        self.anthropic = Anthropic()
        self.tools = []

    def connect(self, server_path: str) -> str:
        return loop.run_until_complete(self._connect(server_path))

    async def _connect(self, server_path: str) -> str:
        if self.exit_stack:
            await self.exit_stack.aclose()

        self.exit_stack = AsyncExitStack()

        is_python = server_path.endswith(".py")
        command = "python" if is_python else "node"

        server_params = StdioServerParameters(
            command=command,
            args=[server_path],
            env={"PYTHONIOENCODING": "utf-8", "PYTHONUNBUFFERED": "1"},
        )

        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()
        self.tools = [
            {
                "name": tool.name,
                "description": tool.description,
                "input_schema": tool.inputSchema,
            }
            for tool in response.tools
        ]

        tool_names = [tool["name"] for tool in self.tools]
        return f"Connected to MCP server. Available tools: {', '.join(tool_names)}"

    def process_message(
        self, message: str, history: List[Union[Dict[str, Any], ChatMessage]]
    ) -> tuple:
        if not self.session:
            return history + [
                {"role": "user", "content": message},
                {
                    "role": "assistant",
                    "content": "Please connect to an MCP server first.",
                },
            ], gr.Textbox(value="")

        new_messages = loop.run_until_complete(self._process_query(message, history))
        return history + [
            {"role": "user", "content": message}
        ] + new_messages, gr.Textbox(value="")

    async def _process_query(
        self, message: str, history: List[Union[Dict[str, Any], ChatMessage]]
    ):
        claude_messages = []
        for msg in history:
            if isinstance(msg, ChatMessage):
                role, content = msg.role, msg.content
            else:
                role, content = msg.get("role"), msg.get("content")

            if role in ["user", "assistant", "system"]:
                claude_messages.append({"role": role, "content": content})

        claude_messages.append({"role": "user", "content": message})

        response = self.anthropic.messages.create(
            # model="claude-3-5-sonnet-20241022",
            model=LLM_MODEL,
            system=SYSTEM_PROMPT,
            max_tokens=1000,
            messages=claude_messages,
            tools=self.tools,
        )

        result_messages = []

        print(response.content)

        for content in response.content:
            if content.type == "text":
                result_messages.append({"role": "assistant", "content": content.text})

            elif content.type == "tool_use":
                tool_id = content.id
                tool_name = content.name
                tool_args = content.input

                result_messages.append(
                    {
                        "role": "assistant",
                        "content": f"I'll use the {tool_name} tool to help answer your question.",
                        "metadata": {
                            "title": f"Using tool: {tool_name}",
                            "log": f"Parameters: {json.dumps(tool_args, ensure_ascii=True)}",
                            "status": "pending",
                            "id": f"tool_call_{tool_name}",
                        },
                    }
                )

                result_messages.append(
                    {
                        "role": "assistant",
                        "content": "```json\n"
                        + json.dumps(tool_args, indent=2, ensure_ascii=True)
                        + "\n```",
                        "metadata": {
                            "parent_id": f"tool_call_{tool_name}",
                            "id": f"params_{tool_name}",
                            "title": "Tool Parameters",
                        },
                    }
                )

                print(f"Calling tool: {tool_name} with args: {tool_args}")
                result = await self.session.call_tool(tool_name, tool_args)

                if result_messages and "metadata" in result_messages[-2]:
                    result_messages[-2]["metadata"]["status"] = "done"

                result_messages.append(
                    {
                        "role": "assistant",
                        "content": "Here are the results from the tool:",
                        "metadata": {
                            "title": f"Tool Result for {tool_name}",
                            "status": "done",
                            "id": f"result_{tool_name}",
                        },
                    }
                )

                result_content = result.content
                print(result_content)
                if isinstance(result_content, list):
                    result_content = "\n".join(str(item) for item in result_content)
                    print("result_content", result_content)

                try:
                    result_json = json.loads(result_content)
                    if isinstance(result_json, dict) and "type" in result_json:
                        if result_json["type"] == "image" and "url" in result_json:
                            result_messages.append(
                                {
                                    "role": "assistant",
                                    "content": {
                                        "path": result_json["url"],
                                        "alt_text": result_json.get(
                                            "message", "Generated image"
                                        ),
                                    },
                                    "metadata": {
                                        "parent_id": f"result_{tool_name}",
                                        "id": f"image_{tool_name}",
                                        "title": "Generated Image",
                                    },
                                }
                            )
                        else:
                            result_messages.append(
                                {
                                    "role": "assistant",
                                    "content": "```\n" + result_content + "\n```",
                                    "metadata": {
                                        "parent_id": f"result_{tool_name}",
                                        "id": f"raw_result_{tool_name}",
                                        "title": "Raw Output",
                                    },
                                }
                            )
                except:
                    result_messages.append(
                        {
                            "role": "assistant",
                            "content": "```\n" + result_content + "\n```",
                            "metadata": {
                                "parent_id": f"result_{tool_name}",
                                "id": f"raw_result_{tool_name}",
                                "title": "Raw Output",
                            },
                        }
                    )

                # claude_messages.append(
                #     {
                #         "role": "user",
                #         "content": f"Tool result for {tool_name}: {result_content}",
                #     }
                # )
                claude_messages.append(
                    {"role": "assistant", "content": [content.model_dump()]}
                )
                claude_messages.append(
                    {
                        "role": "user",
                        "content": [
                            {
                                "type": "tool_result",
                                "tool_use_id": tool_id,
                                "content": result_content,
                            }
                        ],
                    }
                )
                next_response = self.anthropic.messages.create(
                    model=LLM_MODEL,
                    system=SYSTEM_PROMPT,
                    max_tokens=1000,
                    messages=claude_messages,
                )

                print("next_response", next_response.content)

                if next_response.content and next_response.content[0].type == "text":
                    result_messages.append(
                        {"role": "assistant", "content": next_response.content[0].text}
                    )

        return result_messages


client = MCPClientWrapper()


def gradio_interface():
    with gr.Blocks(title="MCP WDI Client") as demo:
        gr.Markdown("# WDI MCP Client")
        gr.Markdown("Connect to the WDI MCP server and chat with the assistant")

        with gr.Row(equal_height=True):
            with gr.Column(scale=4):
                server_path = gr.Textbox(
                    label="Server Script Path",
                    placeholder="Enter path to server script (e.g., wdi_mcp_server.py)",
                    value="wdi_mcp_server.py",
                )
            with gr.Column(scale=1):
                connect_btn = gr.Button("Connect")

        status = gr.Textbox(label="Connection Status", interactive=False)

        chatbot = gr.Chatbot(
            value=[],
            height=500,
            type="messages",
            show_copy_button=True,
            avatar_images=("👤", "🤖"),
        )

        with gr.Row(equal_height=True):
            msg = gr.Textbox(
                label="Your Question",
                placeholder="Ask about what indicators are available for a specific topic (e.g., What's the definition of GDP?)",
                scale=4,
            )
            clear_btn = gr.Button("Clear Chat", scale=1)

        connect_btn.click(client.connect, inputs=server_path, outputs=status)
        msg.submit(client.process_message, [msg, chatbot], [chatbot, msg])
        clear_btn.click(lambda: [], None, chatbot)

    return demo


if __name__ == "__main__":
    if not os.getenv("ANTHROPIC_API_KEY"):
        print(
            "Warning: ANTHROPIC_API_KEY not found in environment. Please set it in your .env file."
        )

    interface = gradio_interface()
    interface.launch(debug=True)