MCPLite MCP Server

@acesanderson

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

acesanderson

关于 MCPLite MCP Server

MCP client/server framework from scratch

MCPLite MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "MCPLite": {
      "command": "python",
      "args": [
        "my_server.py"
      ]
    }
  }
}

MCPLite MCP Server 工具

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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 MCPLite?

MCPLite is a lightweight, pythonic implementation of the Model Context Protocol (MCP) for integrating external tools and data sources into LLM applications. It provides a decorator-based API inspired by FastAPI to turn any Python function into an MCP tool, resource, or prompt. It is designed for Python developers building MCP servers or connecting LLMs to external capabilities.

How to use MCPLite?

Install core dependencies (pydantic, rich) and optional ones for web functionality. Create an MCP server by instantiating MCPLite(transport="stdio") and decorating functions with @mcp.tool, @mcp.resource, or @mcp.prompt. Run the server with mcp.run(). For client orchestration, use the Host class; for interactive chat, use MCPChat. Built-in servers (e.g., fetch, obsidian) can be started via python -m MCPLite.servers.<name>.

Key features of MCPLite

Decorator-based API for tools, resources, and prompts
Multiple transport modes: stdio, direct, and SSE
Built-in servers for web fetching and Obsidian vaults
Host orchestration engine for multi-server coordination
Interactive chat interface with syntax highlighting and tool execution
Pluggable transport layer with configurable logging

Use cases of MCPLite

Building custom MCP servers to expose Python functions as LLM tools
Connecting LLMs to web searches, filesystem operations, or note-taking apps
Orchestrating multiple MCP servers for complex agent workflows
Providing interactive, tool-augmented chat experiences

常见问题

What dependencies does MCPLite require?

Core dependencies are `pydantic` and `rich`. For web functionality, install `requests`, `beautifulsoup4`, and `markdownify`. The `chain-of-thought` package is recommended for stdio transport.

What transport options are available?

MCPLite supports stdio (production), direct (development), and SSE (web) transports. You specify the transport when creating the server: `MCPLite(transport="stdio")`.

How do I configure the Obsidian server?

Set the `OBSIDIAN_PATH` environment variable to your vault’s path. The server then runs sandboxed filesystem operations, searches, and metadata extraction within that vault.

Can MCPLite be extended with custom servers?

Yes. Create a new Python file, instantiate `MCPLite`, and use the decorator pattern (`@mcp.tool`, etc.) to add tools, resources, or prompts. The server can then be run or imported.

Where does MCPLite log information?

Logging is configurable via the `MCPLITE_LOG_LEVEL` environment variable (e.g., `DEBUG`). By default, it logs at the configured level to standard output.

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