What protocols does MCP SSE Client Python support now?
Currently only Server-Sent Events (SSE). Streamable HTTP, WebSocket, and gRPC are planned for future releases.
MCP SSE Client Python
@zanetworker
基本信息
关于 MCP SSE Client Python
Simple MCP Client for remote MCP Servers 🌐
MCP SSE Client Python 配置
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"mcp-sse-client-python": {
"command": "python",
"args": [
"-m",
"mcp_sse_client.examples.llm_example",
"--provider",
"openai",
"--endpoint",
"http://localhost:8000/sse"
]
}
}
}MCP SSE Client Python 工具
工具列表暂未提供
当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
概览
What is MCP SSE Client Python?
MCP SSE Client Python is a Python toolkit for connecting to and interacting with remote Model Context Protocol (MCP) endpoints over Server-Sent Events (SSE). It is designed for developers building AI-driven applications that need to list and invoke MCP tools from a remote server.
How to use MCP SSE Client Python?
Install the package via pip install -e . from source or pip install mcp-playground once published. Set environment variables for LLM provider API keys (e.g., OPENAI_API_KEY). Use the MCPClient class with an endpoint URL ending in /sse. Optionally use OpenAIBridge or other LLM bridges for natural-language tool selection. Run the included Streamlit app with streamlit run app.py for interactive testing.
Key features of MCP SSE Client Python
- Full SSE implementation for real-time remote MCP communication
- LLM-powered tool selection using OpenAI, Anthropic, Ollama, or OpenRouter
- Pythonic
MCPClientwith timeout and retry logic - Interactive Streamlit testing interface with multiple chat modes
- Command-line interface for quick tool listing and invocation
- Automatic error handling with bespoke exception types
Use cases of MCP SSE Client Python
- Connect an AI agent to a remote MCP tool server and let the LLM decide which tool to invoke
- Test and debug a remote MCP endpoint interactively without writing full client code
- Build a chat interface that transparently uses MCP tools when needed
- Scripted automation of MCP tool calls with configurable retry and timeout
常见问题
Does the endpoint URL require a specific format?
Yes. All MCP endpoints must end with `/sse` (e.g., `http://localhost:8000/sse`). The client validates this requirement.
Which LLM providers are supported?
OpenAI, Anthropic, Ollama (local), and OpenRouter. Each has a dedicated bridge class: `OpenAIBridge`, `AnthropicBridge`, `OllamaBridge`, and `OpenRouterBridge`.
What are the runtime requirements?
Python 3.8 or later, plus `mcp>=0.1.0`, `pydantic>=2.0.0`, and optionally `openai`, `anthropic`, `ollama`, and `streamlit` for their respective features.
How does MCP SSE Client Python handle connection errors?
The client includes automatic retry with exponential backoff, configurable timeout (default 30 s), and maximum retries (default 3). Specific exception types (`MCPConnectionError`, `MCPTimeoutError`) are provided.
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