simple mcp client with sampling capability

Author: yarnabrina

examples/clients/simple-sampling-client/.env.example

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+OPENAI_API_KEY=YOUR_OPENAI_API_KEY

examples/clients/simple-sampling-client/.gitignore

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+.env

examples/clients/simple-sampling-client/.python-version

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+3.13

examples/clients/simple-sampling-client/README.md

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+# Simple Sampling Client Example (MCP)
+
+This example demonstrates how to use the sampling capability of the MCP SDK with an OpenAI-compatible client. It shows how to:
+
+- Connect to an MCP server
+- Fetch available tools
+- Use OpenAI's API for chat completions
+- Call MCP tools from the client
+
+## Prerequisites
+
+- Python 3.13+
+- [uv](https://github.com/astral-sh/uv) for dependency management
+- An OpenAI API key (set in a `.env` file or as an environment variable)
+
+## Setup
+
+1. Install dependencies:
+
+	```sh
+	cd examples/clients/simple-sampling-client/
+	uv sync
+	```
+
+2. Set environment variables in a `.env` file. A sample `.env` file is provided as `.env.example`.
+
+3. Start the MCP server in a separate terminal:
+
+	```sh
+	cd examples/snippets/servers/
+	uv run server sampling streamable-http
+	```
+
+4. Run the sampling client in previous terminal:
+
+	```sh
+	uv run mcp-simple-sampling-client
+	```
+
+## Usage
+
+You will be prompted to enter a message. Type your message and press Enter. The assistant will respond using the sampling capability and may call MCP tools as needed.
+
+Type `exit` or `quit` to stop the client.
+
+---
+For more details, see the source code in `mcp_simple_sampling_client/main.py`.

examples/clients/simple-sampling-client/mcp_simple_sampling_client/__init__.py

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+"""Simple sampling client for MCP."""

examples/clients/simple-sampling-client/mcp_simple_sampling_client/main.py

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+"""Define a simple MCP client that supports sampling."""
+
+import asyncio
+import http
+import json
+
+import openai
+import pydantic_settings
+from openai.types.chat import (
+    ChatCompletion,
+    ChatCompletionAssistantMessageParam,
+    ChatCompletionMessageParam,
+    ChatCompletionSystemMessageParam,
+    ChatCompletionToolMessageParam,
+    ChatCompletionToolParam,
+    ChatCompletionUserMessageParam,
+)
+
+import mcp
+from mcp.client.streamable_http import streamablehttp_client
+from mcp.shared.context import RequestContext
+from mcp.types import CreateMessageRequestParams, CreateMessageResult, ErrorData, TextContent, Tool
+
+
+class Configurations(pydantic_settings.BaseSettings):
+    """Define configurations for the sampling client."""
+
+    chat_model: str = "gpt-4o-mini"
+    max_tokens: int = 1024
+    mcp_server_host: str = "localhost"
+    mcp_server_port: int = 8000
+    openai_api_key: str = "your_openai_api_key"
+    system_prompt: str = "You are a helpful assistant."
+
+    model_config = pydantic_settings.SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
+
+
+class SamplingClient:
+    """Define a simple MCP client that supports sampling.
+
+    Parameters
+    ----------
+    config : Configurations
+        The configurations for the sampling client.
+    """
+
+    def __init__(self: "SamplingClient", config: Configurations) -> None:
+        self.config = config
+
+        self.server_url = f"http://{self.config.mcp_server_host}:{self.config.mcp_server_port}/mcp"
+        self.openai_client = openai.OpenAI(api_key=self.config.openai_api_key)
+
+        self.conversation_history: list[ChatCompletionMessageParam] = []
+
+    def get_openai_response(
+        self: "SamplingClient",
+        chat_history: list[ChatCompletionMessageParam],
+        system_prompt: str,
+        max_tokens: int,
+        tools: list[ChatCompletionToolParam] | None = None,
+    ) -> ChatCompletion:
+        """Get a non-streaming response from OpenAI API.
+
+        Parameters
+        ----------
+        chat_history : list[ChatCompletionMessageParam]
+            The chat history to use for the chat completion.
+        system_prompt : str
+            The system prompt to use for the chat completion.
+        max_tokens : int
+            The maximum number of tokens to generate in the response.
+        tools : list[ChatCompletionToolParam] | None, optional
+            The tools to use for the chat completion, by default None.
+
+        Returns
+        -------
+        ChatCompletion
+            The response from the OpenAI API.
+        """
+        updated_chat_history = [
+            ChatCompletionSystemMessageParam(content=system_prompt, role="system"),
+            *chat_history,
+        ]
+
+        extra_arguments = {} if tools is None else {"tool_choice": "auto", "tools": tools}
+
+        chat_completion = self.openai_client.chat.completions.create(
+            messages=updated_chat_history,
+            model=self.config.chat_model,
+            max_completion_tokens=max_tokens,
+            n=1,
+            stream=False,
+            **extra_arguments,
+        )
+
+        return chat_completion
+
+    async def fetch_mcp_tools(self: "SamplingClient") -> list[Tool]:
+        """List available tools."""
+        async with streamablehttp_client(self.server_url) as (read_stream, write_stream, _):
+            async with mcp.ClientSession(read_stream, write_stream) as session:
+                await session.initialize()
+
+                server_tools = await session.list_tools()
+
+        return server_tools.tools
+
+    @staticmethod
+    def convert_to_openai_tools(mcp_tools: list[Tool]) -> list[ChatCompletionToolParam]:
+        """Convert MCP tools to OpenAI tool call parameters.
+
+        Parameters
+        ----------
+        mcp_tools : list[Tool]
+            List of MCP tools to convert.
+
+        Returns
+        -------
+        list[ChatCompletionToolParam]
+            List of OpenAI tool call parameters.
+        """
+        return [
+            ChatCompletionToolParam(
+                function={
+                    "name": tool.name,
+                    "description": tool.description or "",
+                    "parameters": tool.inputSchema,
+                },
+                type="function",
+            )
+            for tool in mcp_tools
+        ]
+
+    async def sampling_handler(
+        self: "SamplingClient", context: RequestContext, parameters: CreateMessageRequestParams
+    ) -> CreateMessageResult | ErrorData:
+        """Handle sampling requests for OpenAI API calls with MCP tools.
+
+        Parameters
+        ----------
+        context : RequestContext
+            request context containing information about the sampling request
+        parameters : CreateMessageRequestParams
+            parameters for the sampling request, including messages and customisations
+
+        Returns
+        -------
+        CreateMessageResult | ErrorData
+            result of the sampling request, either a message result or an error data
+        """
+        del context
+
+        openai_response = self.get_openai_response(
+            [
+                ChatCompletionUserMessageParam(
+                    content=(
+                        message.content.text
+                        if isinstance(message.content, TextContent)
+                        else str(message.content)
+                    ),
+                    role="user",
+                )
+                for message in parameters.messages
+            ],
+            parameters.systemPrompt or self.config.system_prompt,
+            parameters.maxTokens,
+        )
+
+        if not (choices := openai_response.choices):
+            return ErrorData(
+                code=http.HTTPStatus.INTERNAL_SERVER_ERROR,
+                message="No choices returned from OpenAI API.",
+            )
+
+        choice = choices[0]
+        sampling_response_message = choice.message.content or ""
+
+        return CreateMessageResult(
+            role="assistant",
+            content=TextContent(type="text", text=sampling_response_message),
+            model=self.config.chat_model,
+            stopReason=choice.finish_reason,
+        )
+
+    async def execute_tool_call(self: "SamplingClient", tool_name: str, arguments: dict) -> str:
+        """Execute a tool call on an MCP server.
+
+        Parameters
+        ----------
+        tool_name : str
+            name of the tool to call, formatted as "mcp-{server_name}-{tool_name}"
+        arguments : dict
+            arguments to pass to the tool call
+
+        Returns
+        -------
+        str
+            JSON string containing the result of the tool call or an error message
+        """
+        async with streamablehttp_client(self.server_url) as (read_stream, write_stream, _):
+            async with mcp.ClientSession(
+                read_stream, write_stream, sampling_callback=self.sampling_handler
+            ) as session:
+                await session.initialize()
+
+                tool_result = await session.call_tool(tool_name, arguments=arguments)
+
+        if tool_result.isError:
+            error_message = "".join(
+                content.text for content in tool_result.content if isinstance(content, TextContent)
+            )
+
+            return json.dumps(
+                {
+                    "error": (
+                        f"Failed tool call to {tool_name=} with {arguments=}: {error_message}."
+                    )
+                }
+            )
+
+        if (structured_result := tool_result.structuredContent) is not None:
+            return json.dumps(structured_result)
+
+        return json.dumps([element.model_dump() for element in tool_result.content])
+
+    async def orchestrate(self: "SamplingClient", user_message: str) -> None:
+        """Orchestrate the sampling client to handle requests."""
+        self.conversation_history.append(
+            ChatCompletionUserMessageParam(role="user", content=user_message)
+        )
+
+        self.mcp_server_tools = await self.fetch_mcp_tools()
+        self.openai_compatible_tools = self.convert_to_openai_tools(self.mcp_server_tools)
+
+        openai_response = self.get_openai_response(
+            self.conversation_history,
+            self.config.system_prompt,
+            self.config.max_tokens,
+            tools=self.openai_compatible_tools,
+        )
+
+        if not (choices := openai_response.choices):
+            error_message = "No choices returned from OpenAI API."
+            self.conversation_history.append(
+                ChatCompletionAssistantMessageParam(role="assistant", content=error_message)
+            )
+
+            print(error_message)
+
+            return
+
+        choice = choices[0]
+
+        while choice.finish_reason == "tool_calls":
+            for tool_call in choice.message.tool_calls or []:
+                if tool_call.type != "function":
+                    continue
+
+                tool_response = await self.execute_tool_call(
+                    tool_call.function.name, json.loads(tool_call.function.arguments)
+                )
+
+                self.conversation_history.append(
+                    ChatCompletionAssistantMessageParam(
+                        role="assistant",
+                        content=f"Tool {tool_call.id} returned: {tool_response}",
+                    )
+                )
+
+            openai_response = self.get_openai_response(
+                self.conversation_history,
+                self.config.system_prompt,
+                self.config.max_tokens,
+                tools=self.openai_compatible_tools,
+            )
+
+            if not (choices := openai_response.choices):
+                error_message = "No choices returned from OpenAI API."
+                self.conversation_history.append(
+                    ChatCompletionAssistantMessageParam(role="assistant", content=error_message)
+                )
+
+                print(error_message)
+
+                return
+
+            choice = choices[0]
+
+        assistant_message = choice.message.content or ""
+        self.conversation_history.append({"role": "assistant", "content": assistant_message})
+
+        print(f"Assistant: {choice.message.content}")
+
+
+def main():
+    """Run the sampling client."""
+    config = Configurations()
+
+    sampling_client = SamplingClient(config)
+
+    user_message = input("User: ")
+    while user_message.lower() not in {"exit", "quit"}:
+        asyncio.run(sampling_client.orchestrate(user_message))
+
+        user_message = input("User: ")
+
+
+if __name__ == "__main__":
+    main()