Create ARC1 prototype with full MCP client/server setup

prototypes/arc1_prototype/.gitignore

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+.env
+__pycache__
+.venv
+.roproject

prototypes/arc1_prototype/.python-version

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+3.13

prototypes/arc1_prototype/client.py

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+import asyncio
+import json
+import logging
+import os
+import shutil
+from contextlib import AsyncExitStack
+from typing import Any
+from openai import AzureOpenAI
+
+import httpx
+from dotenv import load_dotenv
+from mcp import ClientSession, StdioServerParameters
+from mcp.client.stdio import stdio_client
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+
+
+class Configuration:
+    """Manages configuration and environment variables for the MCP client."""
+
+    def __init__(self) -> None:
+        """Initialize configuration with environment variables."""
+        self.load_env()
+        self.api_key = os.getenv("LLM_API_KEY")
+
+    @staticmethod
+    def load_env() -> None:
+        """Load environment variables from .env file."""
+        load_dotenv()
+
+    @staticmethod
+    def load_config(file_path: str) -> dict[str, Any]:
+        """Load server configuration from JSON file.
+
+        Args:
+            file_path: Path to the JSON configuration file.
+
+        Returns:
+            Dict containing server configuration.
+
+        Raises:
+            FileNotFoundError: If configuration file doesn't exist.
+            JSONDecodeError: If configuration file is invalid JSON.
+        """
+        with open(file_path, "r") as f:
+            return json.load(f)
+
+    @property
+    def llm_api_key(self) -> str:
+        """Get the LLM API key.
+
+        Returns:
+            The API key as a string.
+
+        Raises:
+            ValueError: If the API key is not found in environment variables.
+        """
+        if not self.api_key:
+            raise ValueError("LLM_API_KEY not found in environment variables")
+        return self.api_key
+
+
+class Server:
+    """Manages MCP server connections and tool execution."""
+
+    def __init__(self, name: str, config: dict[str, Any]) -> None:
+        self.name: str = name
+        self.config: dict[str, Any] = config
+        self.stdio_context: Any | None = None
+        self.session: ClientSession | None = None
+        self._cleanup_lock: asyncio.Lock = asyncio.Lock()
+        self.exit_stack: AsyncExitStack = AsyncExitStack()
+
+    async def initialize(self) -> None:
+        """Initialize the server connection."""
+        command = (
+            shutil.which("npx")
+            if self.config["command"] == "npx"
+            else self.config["command"]
+        )
+        if command is None:
+            raise ValueError("The command must be a valid string and cannot be None.")
+
+        server_params = StdioServerParameters(
+            command=command,
+            args=self.config["args"],
+            env={**os.environ, **self.config["env"]}
+            if self.config.get("env")
+            else None,
+        )
+        try:
+            stdio_transport = await self.exit_stack.enter_async_context(
+                stdio_client(server_params)
+            )
+            read, write = stdio_transport
+            session = await self.exit_stack.enter_async_context(
+                ClientSession(read, write)
+            )
+            await session.initialize()
+            self.session = session
+        except Exception as e:
+            logging.error(f"Error initializing server {self.name}: {e}")
+            await self.cleanup()
+            raise
+
+    async def list_tools(self) -> list[Any]:
+        """List available tools from the server.
+
+        Returns:
+            A list of available tools.
+
+        Raises:
+            RuntimeError: If the server is not initialized.
+        """
+        if not self.session:
+            raise RuntimeError(f"Server {self.name} not initialized")
+
+        tools_response = await self.session.list_tools()
+        tools = []
+
+        for item in tools_response:
+            if isinstance(item, tuple) and item[0] == "tools":
+                tools.extend(
+                    Tool(tool.name, tool.description, tool.inputSchema)
+                    for tool in item[1]
+                )
+
+        return tools
+
+    async def execute_tool(
+        self,
+        tool_name: str,
+        arguments: dict[str, Any],
+        retries: int = 2,
+        delay: float = 1.0,
+    ) -> Any:
+        """Execute a tool with retry mechanism.
+
+        Args:
+            tool_name: Name of the tool to execute.
+            arguments: Tool arguments.
+            retries: Number of retry attempts.
+            delay: Delay between retries in seconds.
+
+        Returns:
+            Tool execution result.
+
+        Raises:
+            RuntimeError: If server is not initialized.
+            Exception: If tool execution fails after all retries.
+        """
+        if not self.session:
+            raise RuntimeError(f"Server {self.name} not initialized")
+
+        attempt = 0
+        while attempt < retries:
+            try:
+                logging.info(f"Executing {tool_name}...")
+                result = await self.session.call_tool(tool_name, arguments)
+
+                return result
+
+            except Exception as e:
+                attempt += 1
+                logging.warning(
+                    f"Error executing tool: {e}. Attempt {attempt} of {retries}."
+                )
+                if attempt < retries:
+                    logging.info(f"Retrying in {delay} seconds...")
+                    await asyncio.sleep(delay)
+                else:
+                    logging.error("Max retries reached. Failing.")
+                    raise
+
+    async def cleanup(self) -> None:
+        """Clean up server resources."""
+        async with self._cleanup_lock:
+            try:
+                await self.exit_stack.aclose()
+                self.session = None
+                self.stdio_context = None
+            except Exception as e:
+                logging.error(f"Error during cleanup of server {self.name}: {e}")
+
+
+class Tool:
+    """Represents a tool with its properties and formatting."""
+
+    def __init__(
+        self, name: str, description: str, input_schema: dict[str, Any]
+    ) -> None:
+        self.name: str = name
+        self.description: str = description
+        self.input_schema: dict[str, Any] = input_schema
+
+    def format_for_llm(self) -> str:
+        """Format tool information for LLM.
+
+        Returns:
+            A formatted string describing the tool.
+        """
+        args_desc = []
+        if "properties" in self.input_schema:
+            for param_name, param_info in self.input_schema["properties"].items():
+                arg_desc = (
+                    f"- {param_name}: {param_info.get('description', 'No description')}"
+                )
+                if param_name in self.input_schema.get("required", []):
+                    arg_desc += " (required)"
+                args_desc.append(arg_desc)
+
+        return f"""
+Tool: {self.name}
+Description: {self.description}
+Arguments:
+{chr(10).join(args_desc)}
+"""
+
+
+class LLMClient:
+    """Manages communication with the LLM provider."""
+
+    def __init__(self, api_key: str) -> None:
+        self.api_key: str = api_key
+
+    def get_response(self, messages: list[dict[str, str]]) -> str:
+        """Get a response from the LLM.
+
+        Args:
+            messages: A list of message dictionaries.
+
+        Returns:
+            The LLM's response as a string.
+
+        Raises:
+            httpx.RequestError: If the request to the LLM fails.
+        """
+        url = "https://ai.exxeta.com/api/v2/azure/openai"
+
+        # Convert messages to the correct format expected by the API
+        formatted_messages = []
+        for msg in messages:
+            # print(msg)
+            formatted_messages.append({
+                "role": msg["role"],
+                "content": msg["content"]
+            })
+
+        client = AzureOpenAI(
+            api_key=self.api_key,
+            api_version="2023-07-01-preview",
+            base_url=url
+        )
+        response = client.chat.completions.create(
+                messages=formatted_messages,
+                model="gpt-4o-mini",
+                # response_format={"type": "json_object"}
+                # temperature=0.7,
+                # top_p=0.95,
+                # frequency_penalty=0,
+                # presence_penalty=0,
+                # max_tokens=800,
+                # stop="",
+                # stream=False
+            )
+        if response.choices[0].message.content:
+            # print("response: " + response.choices[0].message.content)
+            return response.choices[0].message.content
+
+        print("No response from Azure OpenAI")
+
+        # url = "https://api.groq.com/openai/v1/chat/completions"
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {self.api_key}",
+        }
+        payload = {
+            "messages": messages,
+            "model": "gpt-4o-mini",
+            "temperature": 0.7,
+            "max_tokens": 4096,
+            "top_p": 1,
+            "stream": False,
+            "stop": None,
+        }
+
+        try:
+            with httpx.Client() as client:
+                response = client.post(url, headers=headers, json=payload)
+                response.raise_for_status()
+                data = response.json()
+                return data["choices"][0]["message"]["content"]
+
+        except httpx.RequestError as e:
+            error_message = f"Error getting LLM response: {str(e)}"
+            logging.error(error_message)
+
+            if isinstance(e, httpx.HTTPStatusError):
+                status_code = e.response.status_code
+                logging.error(f"Status code: {status_code}")
+                logging.error(f"Response details: {e.response.text}")
+
+            return (
+                f"I encountered an error: {error_message}. "
+                "Please try again or rephrase your request."
+            )
+
+
+class ChatSession:
+    """Orchestrates the interaction between user, LLM, and tools."""
+
+    def __init__(self, servers: list[Server], llm_client: LLMClient) -> None:
+        self.servers: list[Server] = servers
+        self.llm_client: LLMClient = llm_client
+
+    async def cleanup_servers(self) -> None:
+        """Clean up all servers properly."""
+        cleanup_tasks = [
+            asyncio.create_task(server.cleanup()) for server in self.servers
+        ]
+        if cleanup_tasks:
+            try:
+                await asyncio.gather(*cleanup_tasks, return_exceptions=True)
+            except Exception as e:
+                logging.warning(f"Warning during final cleanup: {e}")
+
+    async def process_llm_response(self, llm_response: str) -> str:
+        """Process the LLM response and execute tools if needed.
+
+        Args:
+            llm_response: The response from the LLM.
+
+        Returns:
+            The result of tool execution or the original response.
+        """
+        import json
+
+        try:
+            tool_call = json.loads(llm_response)
+            if "tool" in tool_call and "arguments" in tool_call:
+                logging.info(f"Executing tool: {tool_call['tool']}")
+                logging.info(f"With arguments: {tool_call['arguments']}")
+
+                for server in self.servers:
+                    tools = await server.list_tools()
+                    if any(tool.name == tool_call["tool"] for tool in tools):
+                        try:
+                            result = await server.execute_tool(
+                                tool_call["tool"], tool_call["arguments"]
+                            )
+
+                            if isinstance(result, dict) and "progress" in result:
+                                progress = result["progress"]
+                                total = result["total"]
+                                percentage = (progress / total) * 100
+                                logging.info(
+                                    f"Progress: {progress}/{total} "
+                                    f"({percentage:.1f}%)"
+                                )
+
+                            return f"Tool execution result: {result}"
+                        except Exception as e:
+                            error_msg = f"Error executing tool: {str(e)}"
+                            logging.error(error_msg)
+                            return error_msg
+
+                return f"No server found with tool: {tool_call['tool']}"
+            return llm_response
+        except json.JSONDecodeError:
+            return llm_response
+
+    async def start(self) -> None:
+        """Main chat session handler."""
+        try:
+            for server in self.servers:
+                try:
+                    await server.initialize()
+                except Exception as e:
+                    logging.error(f"Failed to initialize server: {e}")
+                    await self.cleanup_servers()
+                    return
+
+            all_tools = []
+            for server in self.servers:
+                tools = await server.list_tools()
+                all_tools.extend(tools)
+
+            tools_description = "\n".join([tool.format_for_llm() for tool in all_tools])
+
+            # print("All tools:", tools_description)
+
+            system_message = (
+                "You are a helpful assistant with access to these tools:\n\n"
+                f"{tools_description}\n"
+                "Choose the appropriate tool based on the user's question. "
+                "If no tool is needed, reply directly.\n\n"
+                "IMPORTANT: When you need to use a tool, you must ONLY respond with "
+                "the exact JSON object format below, nothing else:\n"
+                "{\n"
+                '    "tool": "tool-name",\n'
+                '    "arguments": {\n'
+                '        "argument-name": "value"\n'
+                "    }\n"
+                "}\n\n"
+                "After receiving a tool's response:\n"
+                "1. Transform the raw data into a natural, conversational response\n"
+                "2. Keep responses concise but informative\n"
+                "3. Focus on the most relevant information\n"
+                "4. Use appropriate context from the user's question\n"
+                "5. Avoid simply repeating the raw data\n\n"
+                "Please use only the tools that are explicitly defined above."
+                "You can call another tool, but call them in the next call. Answer 'next tool' if you need another tool."
+            )
+
+            messages = [{"role": "system", "content": system_message}]
+
+            while True:
+                try:
+                    user_input = input("You: ").strip().lower()
+                    if user_input in ["quit", "exit"]:
+                        logging.info("\nExiting...")
+                        break
+
+                    messages.append({"role": "user", "content": user_input})
+
+                    llm_response = self.llm_client.get_response(messages)
+                    logging.info("\nAssistant: %s", llm_response)
+
+                    result = await self.process_llm_response(llm_response)
+
+                    while result != llm_response:
+                        messages.append({"role": "assistant", "content": llm_response})
+                        messages.append({"role": "system", "content": result})
+
+                        llm_response = self.llm_client.get_response(messages)
+                        logging.info("\nAssistant: %s", llm_response)
+
+                        result = await self.process_llm_response(llm_response)
+
+                        # logging.info("\nFinal response: %s", final_response)
+                        # messages.append(
+                        #     {"role": "assistant", "content": final_response}
+                        # )
+
+                    # messages.append({"role": "assistant", "content": llm_response})
+                    # logging.info("\nFinal response: %s", llm_response)
+
+
+                except KeyboardInterrupt:
+                    logging.info("\nExiting...")
+                    break
+
+        finally:
+            await self.cleanup_servers()
+
+
+async def main() -> None:
+    """Initialize and run the chat session."""
+    config = Configuration()
+    server_config = config.load_config("servers_config.json")
+    servers = [
+        Server(name, srv_config)
+        for name, srv_config in server_config["mcpServers"].items()
+    ]
+    llm_client = LLMClient(config.llm_api_key)
+    chat_session = ChatSession(servers, llm_client)
+    await chat_session.start()
+
+if __name__ == "__main__":
+    asyncio.run(main())

prototypes/arc1_prototype/pyproject.toml

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+[project]
+name = "arc1-prototype"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "mcp[cli]>=1.7.1",
+    "openai>=1.77.0",
+]

prototypes/arc1_prototype/server.py

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+# server.py
+from mcp.server.fastmcp import FastMCP
+import random
+
+# Create an MCP server
+mcp = FastMCP("Demo")
+
+risikoProfile = ["Core/Core+, Core", "Value Add"]
+risikoProfileSpacy = ["Core/Core+, Core", "Value Add", "3.2", "e au uae"]
+
+# Add an addition tool
+@mcp.tool()
+def add(a: int, b: int) -> int:
+    """Add two numbers"""
+    return a + b
+
+@mcp.tool()
+def getFromSpaCy() -> list:
+    """Get data from SpaCy"""
+    return [{"page":random.randint(1, 35), "value": random.choice(risikoProfileSpacy), "key": "Risiko"},
+            {"page":random.randint(1, 35), "value": "Real Estate", "key": "FondName"}]
+
+@mcp.tool()
+def getFromChatGPT() -> list:
+    """Get data from ChatGPT"""
+    return [{"page":random.randint(1, 35), "value": random.choice(risikoProfile), "key": "Risiko"},
+            {"page":random.randint(1, 35), "value": "Real False Name", "key": "FondName"}]
+
+@mcp.tool()
+def checkSpacyResult() -> dict:
+    """This tool checks the result of SpaCy, ensuring it meets certain criteria."""
+    return {"page":random.randint(1, 35), "value": random.choice(risikoProfile), "key": "Risiko"}
+
+@mcp.tool()
+def getFromChatGPTSingle(value: str) -> dict:
+    """This tool get a single value from ChatGPT. You can use the value to specify for which key the value should calculated"""
+    return {"page":random.randint(1, 35), "value": random.choice(risikoProfile), "key": value}
+
+# Add a dynamic greeting resource
+@mcp.resource("greeting://{name}")
+def get_greeting(name: str) -> str:
+    """Get a personalized greeting"""
+    return f"Hello, {name}!"

prototypes/arc1_prototype/servers_config.json

@@ -0,0 +1,8 @@
+{
+	"mcpServers": {
+		"sqlite": {
+			"command": "uv",
+			"args": ["run", "mcp", "run", "server.py"]
+		}
+	}
+}

prototypes/arc1_prototype/uv.lock

@@ -0,0 +1,424 @@
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+revision = 2
+requires-python = ">=3.13"
+
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+]
+
+[[package]]
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+version = "4.9.0"
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+]
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+[[package]]
+name = "arc1-prototype"
+version = "0.1.0"
+source = { virtual = "." }
+dependencies = [
+    { name = "mcp", extra = ["cli"] },
+    { name = "openai" },
+]
+
+[package.metadata]
+requires-dist = [
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+    { name = "openai", specifier = ">=1.77.0" },
+]
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