一组为本地大型语言模型构建的模型上下文协议（MCP）工具。一个 venv，多种选择。

为什么它存在

许多MCP服务器分散为独立项目，需要单独设置。 该工具包将几台本地MCP服务器保存在一个仓库和一个uv环境中。

特色

• MCP JSON 配置生成：运行 main.py 并通过向导操作
• 一个venv用于多个MCP服务器

MCP服务器

• 网页搜索：使用duckduckgo作为搜索引擎，获取并总结热门结果
• Python 沙盒：允许代理运行 Python，并使用 NumPy 和 SymPy 进行数学任务
• 长期记忆：存储轻量级长期票据

安全说明

1. 默认传输是stdio。你可以在GlobalConfig中切换到HTTP。
2. python-sandbox.py 使用 exec() 和 eval() 来表示 run_python。这不是一个安全的沙盒。
3. 将run_python视为代理在您的用户账户下执行的本地代码。
4. 生成的配置现在将 PYTHON_SANDBOX_LOG_PATH 设置为本地 python-sandbox 条目，因此每次 run_python 执行都会附加到 JSONL 审计日志中。

要求

• Python >= 3.13
• 管理方式为uv

安装

使用紫外线：

生成 LM Studio 配置

跑步：

Local MCP Tools Collection

A small collection of Model Context Protocol (MCP) tools, built for local LLMs. One venv, many options.

Why it exists

Many MCP servers are distributed as separate projects and need separate setup. This tool pack keeps a few local MCP servers in one repo and one uv environment.

Features

• MCP JSON configuration generation: run main.py and go through the wizard
• One venv for multiple MCP servers

MCP Servers

• Web Search: Use duckduckgo as search engine, fetch and summarize top results
• Python SandBox: Allow agents to run Python and use NumPy and SymPy for math tasks
• Longterm-Memory: Store lightweight long-term notes

Security Notes

1. Default transport is stdio. You can switch to HTTP in GlobalConfig.
2. python-sandbox.py uses exec() and eval() for run_python. This is not a secure sandbox.
3. Treat run_python as local code execution by the agent under your user account.
4. The generated config now sets PYTHON_SANDBOX_LOG_PATH for local python-sandbox entries so every run_python execution is appended to a JSONL audit log.

Requirements

• Python >= 3.13
• Managed with uv

Install

Using uv:

uv sync

Generate LM Studio Config

Run:

uv run python main.py

LM Studio currently follows Cursor-style mcp.json notation. The generated LM Studio output uses the mcpServers object with either:

• command + args + env for local stdio servers
• url + headers for remote HTTP servers

If you select the local python-sandbox server, the wizard will ask for an audit log file path.

Run the MCP Server

python python-sandbox.py

The server communicates over stdio (FastMCP). Point your MCP-compatible client at the executable command above.

Tool Usage Examples

Run main.py for JSON configuration generation. For LM Studio, you will get something like this:

{
  "mcpServers": {
    "memory": {
      "command": "E:\\LMStudio\\mcp\\lmstudio-toolpack\\.venv\\Scripts\\python.exe",
      "args": [
        "E:\\LMStudio\\mcp\\lmstudio-toolpack\\MCPs\\Memory.py"
      ],
      "env": {}
    },
    "python-sandbox": {
      "command": "E:\\LMStudio\\mcp\\lmstudio-toolpack\\.venv\\Scripts\\python.exe",
      "args": [
        "E:\\LMStudio\\mcp\\lmstudio-toolpack\\MCPs\\python-sandbox.py"
      ],
      "env": {
        "PYTHON_SANDBOX_LOG_PATH": "E:\\LMStudio\\mcp\\lmstudio-toolpack\\data\\python-sandbox-audit.jsonl"
      }
    },
    "websearch": {