Mcpsampling
A comprehensive demonstration of the Model Context Protocol (MCP) using Spring AI with multiple AI providers including OpenAI and Ollama. This project showcases how to build both MCP servers and clien
- Transport
- stdio
- Status
- Local
エージェントを強化
このサーバーを追加
Install from the publisher. Not a PlugMCP endpoint.
RepositoryPublisher installStep-by-step: add this in Claude
Remote URL or a command on your machine? How the two installs differ
PlugMCP で実行
キーとログ付きでコピーをホスト。
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
起動Server facts
Local- Transport
- stdio
- Last checked
- 2026-10-26
- Stars
- 3
- Tools
- —
- Uptime
- —
Overview
About Mcpsampling
A comprehensive demonstration of the Model Context Protocol (MCP) using Spring AI with multiple AI providers including OpenAI and Ollama. This project showcases how to build both MCP servers and clients that can route requests to different Large Language Models (LLMs) based on model preferences.
Tools
Tools
No tool list yet. Remote servers get one after the first health check.
FAQ
Questions
How do I install Mcpsampling?
Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.
Does Mcpsampling need an API key?
No required environment variable is listed on this record.
What transport does Mcpsampling use?
Mcpsampling runs locally over stdio.
When was Mcpsampling last checked?
Last checked 2026-10-26. A comprehensive demonstration of the Model Context Protocol (MCP) using Spring AI with multiple AI providers including OpenAI and Ollama. This project showcases how to build both MCP servers and clients that can route requests to different Large Language Models (LLMs) based on model preferences.
From the publisher
Documentation
README is not crawled yet. Open the repository
A comprehensive demonstration of the Model Context Protocol (MCP) using Spring AI with multiple AI providers including OpenAI and Ollama. This project showcases how to build both MCP servers and clients that can route requests to different Large Language Models (LLMs) based on model preferences.