mcp-research-server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-research-servershow me available research folders"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP ChatBot L9 —— 混合传输模式
在 L7 的基础上,将 research server 从本地 stdio 子进程改为远程 SSE 服务器。
架构变化
组件 | L7 | L9 |
research server | stdio 子进程 | SSE 远程 (port 8001) |
filesystem server | stdio 子进程 | stdio 子进程 (不变) |
fetch server | stdio 子进程 | stdio 子进程 (不变) |
LLM API | DeepSeek (openai SDK) | DeepSeek (不变) |
Related MCP server: SSH MCP Remote
运行方式
# 1. 启动远程 SSE 服务器(终端 1)
uv run research_server.py
# 2. 启动聊天机器人(终端 2)
uv run mcp_chatbot.py命令
命令 | 说明 |
| 查看可用论文主题文件夹 |
| 查看某主题的论文列表 |
| 列出所有提示模板 |
| 执行提示模板 |
| 退出对话 |
🚀 部署到 Render(远程 SSE 服务)
1. 创建 GitHub 仓库
在 GitHub 创建新仓库(例如 mcp-research-server),不要勾选 "Add a README file"。
2. 推送代码
cd L9/mcp_project
# 初始化 git
git init
git add .
git commit -m "Initial commit: MCP Research Server (SSE)"
# 关联远程仓库并推送
git remote add origin https://github.com/<你的用户名>/mcp-research-server.git
git branch -M main
git push -u origin main3. Render 关联部署
登录 Render Dashboard(用 GitHub 账号)
点击 New + → Web Service
选择刚推送的 GitHub 仓库
Render 自动检测
render.yaml,或者手动配置:Build Command:
pip install -r requirements.txtStart Command:
python research_server.py
点击 Deploy Web Service
4. 更新客户端配置
部署成功后,Render 会分配一个 URL(如 https://mcp-research-server.onrender.com)。
修改 server_config.json 中的 research 服务器 URL:
{
"mcpServers": {
"research": {
"transport": "sse",
"url": "https://mcp-research-server.onrender.com/sse"
}
}
}5. 测试远程服务
# 启动 chatbot(现在连接到远程 Render 服务器)
uv run mcp_chatbot.py注意: Render 免费层在无流量时会休眠,首次请求可能需要 30-60 秒唤醒。
项目文件说明
文件 | 用途 |
| FastMCP SSE 服务器(端口由 PORT 环境变量控制) |
| 混合传输客户端(sse_client + stdio_client) |
| MCP 服务器连接配置 |
| Render Blueprint 部署配置 |
| Render 部署用的 pip 依赖 |
| 本地开发用 uv 依赖 |
| 论文数据存储目录 |
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