Mcp_proxy_pydantic_agent

好的，以下是将 MCP 服务器暴露给 Pydantic Agents 的一个例子： **英文:** Here's an example of exposing MCP (Model Control Plane) servers to Pydantic Agents: **Conceptual Overview:** 1. **MCP Server:** Imagine you have an MCP server that manages machine learning models. It might provide endpoints for: * Listing available models. * Getting model metadata (e.g., input/output schema). * Making predictions using a specific model. 2. **Pydantic Agent:** You want to create a Pydantic Agent that can interact with this MCP server. The agent should be able to: * Discover available models. * Choose the appropriate model for a given task. * Call the model with the correct input format. * Process the model's output. 3. **Pydantic Models for MCP Interaction:** You'll define Pydantic models to represent the data structures used when communicating with the MCP server. This includes: * A model for representing a model's metadata (name, description, input schema, output schema). * A model for representing the input to a model. * A model for representing the output from a model. 4. **Agent Tools:** You'll create agent tools that use these Pydantic models to interact with the MCP server. These tools will handle the HTTP requests to the MCP server and parse the responses into Pydantic objects. **Simplified Example (using Python and `requests` library):** ```python from pydantic import BaseModel, Field from typing import List, Dict, Any import requests # 1. Pydantic Models for MCP Data class ModelMetadata(BaseModel): name: str = Field(..., description="The name of the model.") description: str = Field(..., description="A description of the model.") input_schema: Dict[str, Any] = Field(..., description="The input schema of the model.") output_schema: Dict[str, Any] = Field(..., description="The output schema of the model.") class ModelInput(BaseModel): data: Dict[str, Any] = Field(..., description="The input data for the model.") class ModelOutput(BaseModel): result: Any = Field(..., description="The output from the model.") # 2. Agent Tools for MCP Interaction class MCPClient: def __init__(self, mcp_server_url: str): self.mcp_server_url = mcp_server_url def list_models(self) -> List[ModelMetadata]: """Lists all available models on the MCP server.""" response = requests.get(f"{self.mcp_server_url}/models") response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) model_data = response.json() return [ModelMetadata(**model) for model in model_data] def get_model_metadata(self, model_name: str) -> ModelMetadata: """Gets the metadata for a specific model.""" response = requests.get(f"{self.mcp_server_url}/models/{model_name}") response.raise_for_status() model_data = response.json() return ModelMetadata(**model_data) def predict(self, model_name: str, input_data: ModelInput) -> ModelOutput: """Makes a prediction using the specified model.""" response = requests.post(f"{self.mcp_server_url}/models/{model_name}/predict", json=input_data.dict()) response.raise_for_status() output_data = response.json() return ModelOutput(**output_data) # 3. Example Usage (Conceptual - needs integration with a Pydantic Agent framework) # Assuming you have a Pydantic Agent framework set up (e.g., using LangChain) # Initialize the MCP client mcp_client = MCPClient(mcp_server_url="http://your-mcp-server.com") # Example: List available models available_models = mcp_client.list_models() print("Available Models:", available_models) # Example: Get metadata for a specific model if available_models: first_model_name = available_models[0].name model_metadata = mcp_client.get_model_metadata(first_model_name) print("Model Metadata:", model_metadata) # Example: Make a prediction input_data = ModelInput(data={"feature1": 1.0, "feature2": 2.0}) # Replace with actual input data prediction = mcp_client.predict(first_model_name, input_data) print("Prediction:", prediction) # In a real Pydantic Agent setup, you would: # 1. Define tools that wrap the MCPClient methods. # 2. Use the Pydantic models to define the input and output types of the tools. # 3. Register these tools with your Pydantic Agent. # 4. The agent can then use these tools to interact with the MCP server based on the user's instructions. ``` **Explanation:** * **Pydantic Models:** `ModelMetadata`, `ModelInput`, and `ModelOutput` define the structure of the data exchanged with the MCP server. The `Field` annotations provide descriptions that can be used by the agent framework for tool selection and parameter validation. * **MCPClient:** This class encapsulates the logic for interacting with the MCP server. It uses the `requests` library to make HTTP requests and parses the responses into Pydantic objects. * **Example Usage:** This section shows how you would use the `MCPClient` to interact with the MCP server. It's a simplified example and would need to be integrated with a Pydantic Agent framework like LangChain. * **Important Considerations for Pydantic Agents:** * **Tool Definition:** You would need to define tools that wrap the `MCPClient` methods. Each tool would have a name, description, and input/output types defined using Pydantic models. * **Agent Integration:** You would register these tools with your Pydantic Agent framework. The agent would then be able to use these tools to interact with the MCP server based on the user's instructions. * **Error Handling:** The `response.raise_for_status()` method is used for basic error handling. You might need to add more sophisticated error handling to your agent. * **Authentication:** If your MCP server requires authentication, you would need to add authentication logic to the `MCPClient`. **中文:** 以下是将 MCP (模型控制平面) 服务器暴露给 Pydantic Agents 的一个例子： **概念概述:** 1. **MCP 服务器:** 假设你有一个 MCP 服务器，用于管理机器学习模型。它可能提供以下端点： * 列出可用的模型。 * 获取模型元数据（例如，输入/输出模式）。 * 使用特定模型进行预测。 2. **Pydantic Agent:** 你想创建一个可以与此 MCP 服务器交互的 Pydantic Agent。该 Agent 应该能够： * 发现可用的模型。 * 为给定的任务选择合适的模型。 * 使用正确的输入格式调用模型。 * 处理模型的输出。 3. **用于 MCP 交互的 Pydantic 模型:** 你将定义 Pydantic 模型来表示与 MCP 服务器通信时使用的数据结构。 这包括： * 一个用于表示模型元数据的模型（名称、描述、输入模式、输出模式）。 * 一个用于表示模型输入的模型。 * 一个用于表示模型输出的模型。 4. **Agent 工具:** 你将创建 Agent 工具，这些工具使用这些 Pydantic 模型与 MCP 服务器交互。 这些工具将处理到 MCP 服务器的 HTTP 请求，并将响应解析为 Pydantic 对象。 **简化示例 (使用 Python 和 `requests` 库):** ```python from pydantic import BaseModel, Field from typing import List, Dict, Any import requests # 1. 用于 MCP 数据的 Pydantic 模型 class ModelMetadata(BaseModel): name: str = Field(..., description="模型的名称。") description: str = Field(..., description="模型的描述。") input_schema: Dict[str, Any] = Field(..., description="模型的输入模式。") output_schema: Dict[str, Any] = Field(..., description="模型的输出模式。") class ModelInput(BaseModel): data: Dict[str, Any] = Field(..., description="模型的输入数据。") class ModelOutput(BaseModel): result: Any = Field(..., description="模型的输出。") # 2. 用于 MCP 交互的 Agent 工具 class MCPClient: def __init__(self, mcp_server_url: str): self.mcp_server_url = mcp_server_url def list_models(self) -> List[ModelMetadata]: """列出 MCP 服务器上所有可用的模型。""" response = requests.get(f"{self.mcp_server_url}/models") response.raise_for_status() # 为错误的响应（4xx 或 5xx）引发 HTTPError model_data = response.json() return [ModelMetadata(**model) for model in model_data] def get_model_metadata(self, model_name: str) -> ModelMetadata: """获取特定模型的元数据。""" response = requests.get(f"{self.mcp_server_url}/models/{model_name}") response.raise_for_status() model_data = response.json() return ModelMetadata(**model_data) def predict(self, model_name: str, input_data: ModelInput) -> ModelOutput: """使用指定的模型进行预测。""" response = requests.post(f"{self.mcp_server_url}/models/{model_name}/predict", json=input_data.dict()) response.raise_for_status() output_data = response.json() return ModelOutput(**output_data) # 3. 示例用法 (概念性的 - 需要与 Pydantic Agent 框架集成) # 假设你已经设置了一个 Pydantic Agent 框架（例如，使用 LangChain） # 初始化 MCP 客户端 mcp_client = MCPClient(mcp_server_url="http://your-mcp-server.com") # 示例：列出可用的模型 available_models = mcp_client.list_models() print("可用的模型:", available_models) # 示例：获取特定模型的元数据 if available_models: first_model_name = available_models[0].name model_metadata = mcp_client.get_model_metadata(first_model_name) print("模型元数据:", model_metadata) # 示例：进行预测 input_data = ModelInput(data={"feature1": 1.0, "feature2": 2.0}) # 替换为实际的输入数据 prediction = mcp_client.predict(first_model_name, input_data) print("预测:", prediction) # 在实际的 Pydantic Agent 设置中，你需要： # 1. 定义包装 MCPClient 方法的工具。 # 2. 使用 Pydantic 模型定义工具的输入和输出类型。 # 3. 将这些工具注册到你的 Pydantic Agent。 # 4. 然后，Agent 可以使用这些工具根据用户的指令与 MCP 服务器交互。 ``` **解释:** * **Pydantic 模型:** `ModelMetadata`、`ModelInput` 和 `ModelOutput` 定义了与 MCP 服务器交换的数据的结构。 `Field` 注释提供了描述，Agent 框架可以使用这些描述进行工具选择和参数验证。 * **MCPClient:** 此类封装了与 MCP 服务器交互的逻辑。 它使用 `requests` 库发出 HTTP 请求并将响应解析为 Pydantic 对象。 * **示例用法:** 本节展示了如何使用 `MCPClient` 与 MCP 服务器交互。 这是一个简化的示例，需要与像 LangChain 这样的 Pydantic Agent 框架集成。 * **Pydantic Agents 的重要注意事项:** * **工具定义:** 你需要定义包装 `MCPClient` 方法的工具。 每个工具都将具有一个名称、描述以及使用 Pydantic 模型定义的输入/输出类型。 * **Agent 集成:** 你需要将这些工具注册到你的 Pydantic Agent 框架。 然后，Agent 将能够使用这些工具根据用户的指令与 MCP 服务器交互。 * **错误处理:** `response.raise_for_status()` 方法用于基本的错误处理。 你可能需要在你的 Agent 中添加更复杂的错误处理。 * **身份验证:** 如果你的 MCP 服务器需要身份验证，你需要将身份验证逻辑添加到 `MCPClient`。 This translation aims to be accurate and maintain the technical nuances of the original English text. It also provides explanations to help understand the code and concepts.