mcp_remote_client.py

import asyncio
import os
import json
from typing import List, Dict, Any, Union
from contextlib import AsyncExitStack
from datetime import datetime
import gradio as gr
from gradio.components.chatbot import ChatMessage
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
from mcp.client.sse import sse_client
from anthropic import Anthropic
from anthropic._exceptions import OverloadedError
from dotenv import load_dotenv


load_dotenv()

SYSTEM_PROMPT = f"""You are a helpful assistant. Today is {datetime.now().strftime("%Y-%m-%d")}.

You **do not** have prior knowledge of the World Development Indicators (WDI) data. Instead, you must rely entirely on the tools available to you to answer the user's questions.

When responding you must always plan the steps and enumerate all the tools that you plan to use to answer the user's query.

### Your Instructions:

1. **Tool Use Only**:
   - You must not provide any answers based on prior knowledge or assumptions.
   - You must **not** fabricate data or simulate the behavior of the `get_wdi_data` tool.
   - You cannot use the `get_wdi_data` tool without using the `search_relevant_indicators` tool first.
   - If the user requests WDI data, you **MUST ALWAYS** first call the `search_relevant_indicators` tool to see if there's any relevant data.
   - If relevant data exists, call the `get_wdi_data` tool to get the data.

2. **Tool Invocation**:
   - Use any relevant tools provided to you to answer the user's question.
   - You may call multiple tools if needed, and you should do so in a logical sequence to minimize unnecessary user interaction.
   - Do not hesitate to invoke tools as soon as they are relevant.

3. **Limitations**:
   - If a user request cannot be fulfilled using the tools available, respond by clearly stating that you do not have access to that information.

4. **Ethical Guidelines**:
   - Do not make or endorse statements based on stereotypes, bias, or assumptions.
   - Ensure all claims and explanations are grounded in the data or factual evidence retrieved via tools.
   - Politely refuse to respond to requests that involve stereotypes or unfounded generalizations.

5. **Communication Style**:
   - Present the data in clear, user-friendly language.
   - You may summarize or explain the data retrieved, but do **not** elaborate based on outside or implicit knowledge.
   - You may describe the data in a way that is easy to understand but you MUST NOT elaborate based on external knowledge.

Stay strictly within these boundaries while maintaining a helpful and respectful tone."""


LLM_MODEL = "claude-3-5-haiku-20241022"
# What is the military spending of bangladesh in 2014?
# When a tool is needed for any step, ensure to add the token `TOOL_USE`.


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


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

    async def connect(self, server_path_or_url: str) -> str:
        # If there's an existing session, close it
        if self.exit_stack:
            await self.exit_stack.aclose()

        self.exit_stack = AsyncExitStack()

        if server_path_or_url.endswith(".py"):
            command = "python"

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

            # Launch MCP subprocess and bind streams on the current running loop
            stdio_transport = await self.exit_stack.enter_async_context(
                stdio_client(server_params)
            )
            self.stdio, self.write = stdio_transport
        else:
            sse_transport = await self.exit_stack.enter_async_context(
                sse_client(
                    server_path_or_url,
                    headers={"Authorization": f"Bearer {os.getenv('HF_TOKEN')}"},
                )
            )
            self.stdio, self.write = sse_transport

        # Create ClientSession on this same loop
        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
        ]

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

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

            yield messages, gr.Textbox(value="")

            async for partial in self._process_query(message, history):
                messages.extend(partial)

                yield messages, gr.Textbox(value="")

                if (
                    messages[-1]["role"] == "assistant"
                    and messages[-1]["content"]
                    == "The LLM API is overloaded now, try again later..."
                ):
                    break

        with open("messages.log.jsonl", "a+") as fl:
            fl.write(json.dumps(dict(time=f"{datetime.now()}", messages=messages)))

    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})

        try:
            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,
            )
        except OverloadedError:
            yield [
                {
                    "role": "assistant",
                    "content": "The LLM API is overloaded now, try again later...",
                }
            ]
            # TODO: Add a retry mechanism

        result_messages = []
        partial_messages = []

        print(response.content)
        contents = response.content

        MAX_CALLS = 10
        auto_calls = 0

        while len(contents) > 0 and auto_calls < MAX_CALLS:
            content = contents.pop(0)

            if content.type == "text":
                result_messages.append({"role": "assistant", "content": content.text})
                claude_messages.append({"role": "assistant", "content": content.text})
                partial_messages.append(result_messages[-1])
                yield [result_messages[-1]]
                partial_messages = []

            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.replace('avsolatorio_test_data_mcp_server', '')}",
                            "log": f"Parameters: {json.dumps(tool_args, ensure_ascii=True)}",
                            "status": "pending",
                            "id": f"tool_call_{tool_name}",
                        },
                    }
                )
                partial_messages.append(result_messages[-1])
                yield [result_messages[-1]]

                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",
                        },
                    }
                )
                partial_messages.append(result_messages[-1])
                yield [result_messages[-1]]

                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.replace('avsolatorio_test_data_mcp_server', '')}",
                            "status": "done",
                            "id": f"result_{tool_name}",
                        },
                    }
                )
                partial_messages.append(result_messages[-1])
                yield [result_messages[-1]]
                partial_messages = []

                result_content = result.content
                print(result_content)
                if isinstance(result_content, list):
                    result_content = [r.model_dump() for r in result_content]

                    for r in result_content:
                        # Remove annotations field from each item if it exists
                        r.pop("annotations", None)
                        try:
                            r["text"] = json.loads(r["text"])
                        except:
                            pass

                    print("result_content", result_content)

                result_messages.append(
                    {
                        "role": "assistant",
                        "content": "```\n"
                        + json.dumps(result_content, indent=2)
                        + "\n```",
                        "metadata": {
                            "parent_id": f"result_{tool_name}",
                            "id": f"raw_result_{tool_name}",
                            "title": "Raw Output",
                        },
                    }
                )
                partial_messages.append(result_messages[-1])
                yield [result_messages[-1]]
                partial_messages = []

                claude_messages.append(
                    {"role": "assistant", "content": [content.model_dump()]}
                )
                claude_messages.append(
                    {
                        "role": "user",
                        "content": [
                            {
                                "type": "tool_result",
                                "tool_use_id": tool_id,
                                "content": json.dumps(result_content, indent=2),
                            }
                        ],
                    }
                )

                try:
                    next_response = self.anthropic.messages.create(
                        model=LLM_MODEL,
                        system=SYSTEM_PROMPT,
                        max_tokens=1000,
                        messages=claude_messages,
                        tools=self.tools,
                    )
                    auto_calls += 1
                except OverloadedError:
                    yield [
                        {
                            "role": "assistant",
                            "content": "The LLM API is overloaded now, try again later...",
                        }
                    ]

                print("next_response", next_response.content)

                contents.extend(next_response.content)


def gradio_interface(
    server_path_or_url: str = "https://avsolatorio-test-data-mcp-server.hf.space/gradio_api/mcp/sse",
):
    # server_path_or_url = "https://avsolatorio-test-data-mcp-server.hf.space/gradio_api/mcp/sse"
    # server_path_or_url = "wdi_mcp_server.py"

    client = MCPClientWrapper()

    with gr.Blocks(title="WDI MCP Client") as demo:
        gr.Markdown("## Ask about the World Development Indicators (WDI) data")
        # gr.Markdown("Connect to the WDI MCP server and chat with the assistant")

        with gr.Accordion(
            "Connect to the WDI MCP server and chat with the assistant",
            open=False,
            visible=server_path_or_url.endswith(".py"),
        ):
            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=server_path_or_url,
                    )
                with gr.Column(scale=1):
                    connect_btn = gr.Button("Connect")

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

        chatbot = gr.Chatbot(
            value=[],
            height=600,
            type="messages",
            show_copy_button=True,
            avatar_images=("img/small-user.png", "img/small-robot.png"),
            autoscroll=True,
        )

        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)
        # Automatically call client.connect(...) as soon as the interface loads
        demo.load(fn=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(server_name=os.getenv("SERVER_NAME", "127.0.0.1"), debug=True)