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MCP SSE Client Python

@zanetworker

Repository

Basic information

Category

Other

License

MIT

Transports

stdio

Author

zanetworker

About MCP SSE Client Python

Simple MCP Client for remote MCP Servers 🌐

#mcp-sse-client#python-client#server-sent-events

MCP SSE Client Python config

Add this server to your MCP-compatible client using the configuration below.

{
  "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 tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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 MCPClient with 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

Frequently asked questions

1

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.

2

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.

3

Which LLM providers are supported?

OpenAI, Anthropic, Ollama (local), and OpenRouter. Each has a dedicated bridge class: `OpenAIBridge`, `AnthropicBridge`, `OllamaBridge`, and `OpenRouterBridge`.

4

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.

5

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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