AI导哈

AI文件系统管理器

基于Model Context Protocol (MCP)的AI驱动文件系统管理工具，提供自然语言文件操作和Streamlit Web界面。

  • by chakorabdellatif
  • Python
  • 本地部署
  • 更新于
在 GitHub 查看 chakorabdellatif/filesystem-mcp-with-FastMCP-server

使用说明

MCP Filesystem Assistant

AI-powered filesystem manager built on the Model Context Protocol (MCP), with a FastMCP server, a Streamlit web UI, and OpenAI function-calling for natural-language file operations.

Overview

This project demonstrates a full MCP client/server stack:

  • A FastMCP server (server/filesystem_mcp_server.py) that exposes 8 filesystem tools over SSE transport, sandboxed to a workspace/ directory with path-traversal protection.
  • A Streamlit host application (host/app.py) with a chat tab (OpenAI GPT function-calling drives tool selection), a file browser tab, and a quick-actions tab for direct file operations without going through the LLM.
  • An MCP connector (host/mcp_connector.py) that discovers tools from the server, converts their schemas to OpenAI's function-calling format, and executes tool calls over a fresh SSE client connection per call.

It was built as a learning project for understanding how MCP servers, MCP clients, and an LLM front-end fit together in practice.

Features

  • 8 filesystem tools: read_file, write_file, append_file, delete_file, list_directory, create_directory, move_file, get_file_info — all implemented in server/filesystem_mcp_server.py.
  • Sandboxed workspace: every tool call resolves its path against WORKSPACE_DIR and rejects absolute paths or any path that resolves outside the workspace (validate_path()).
  • Natural-language interface: the Streamlit chat tab sends user messages to OpenAI with the MCP tools exposed as function-calling tools; when the model requests a tool call, the connector executes it against the live MCP server and feeds the result back for a final answer.
  • File browser tab: lists workspace contents in a table, with buttons to view file content or inspect metadata (size, created/modified timestamps).
  • Quick actions tab: create a file, create a directory, or delete a file directly through the UI, bypassing the LLM.
  • Connection status + tool discovery in the sidebar, plus a manual "check connection" and "refresh files" control.

Not implemented

The server module's docstring and startup banner mention a 9th tool (health_check) and a PDF resource — neither is actually present in the code. requirements.txt includes pypdf2 but no PDF-handling code exists anywhere in the repository. This README describes only what is actually implemented (the 8 tools above); the extra banner text in filesystem_mcp_server.py is left as-is but should not be taken as a feature list.

Tech Stack

Layer Technology
MCP server framework FastMCP
Transport SSE (Server-Sent Events)
LLM OpenAI (gpt-4-turbo-preview by default, via function calling)
Web UI Streamlit
Data display pandas
Config python-dotenv

Architecture

┌──────────────────┐        ┌───────────────────┐        ┌────────────────────┐
│  Streamlit UI     │  SSE   │  FastMCP server    │  I/O   │  workspace/         │
│  host/app.py       │◄─────►│  server/filesystem_ │◄─────►│  sandboxed files    │
│  + mcp_connector.py│        │  mcp_server.py      │        │                    │
└─────────┬─────────┘        └───────────────────┘        └────────────────────┘
          │
          │ function-calling
          ▼
   ┌───────────────┐
   │  OpenAI API    │
   └───────────────┘

The Streamlit app and the MCP server are separate processes that must both be running — the UI talks to the server over HTTP/SSE, not via direct function calls.

Getting Started

Prerequisites

  • Python 3.10+
  • An OpenAI API key (only required for the chat tab; the file browser and quick actions tabs work without it once the MCP server is running)

Installation

git clone https://github.com/chakorabdellatif/filesystem-mcp-with-FastMCP-server.git
cd filesystem-mcp-with-FastMCP-server

python -m venv venv
source venv/bin/activate      # Windows: venv\Scripts\activate

pip install -r requirements.txt

Configuration

Copy .env.example to .env and fill in your key:

MCP_SERVER_HOST=127.0.0.1
MCP_SERVER_PORT=8000
OPENAI_API_KEY=your_api_key_here

Run

Terminal 1 — start the MCP server:

python server/filesystem_mcp_server.py

Terminal 2 — launch the Streamlit UI:

streamlit run host/app.py

The UI opens at http://localhost:8501; the MCP server listens on http://127.0.0.1:8000 (SSE endpoint at /sse).

Testing / CI

There is no automated test suite in this repository. CI (.github/workflows/ci.yml) runs a lightweight, fast check on every push/PR:

  • python -m py_compile over every Python module (catches syntax errors)
  • flake8 --select=E9,F63,F7,F82 (catches undefined names and other critical errors, without enforcing style)

Both checks were run locally before this workflow was added and pass cleanly.

Project Structure

filesystem-mcp-with-FastMCP-server/
├── host/
│   ├── app.py               # Streamlit UI (3 tabs: chat, file browser, quick actions)
│   ├── mcp_connector.py     # MCP client + OpenAI function-calling glue
│   └── ui_components.py     # UI rendering helpers / custom CSS
├── server/
│   ├── filesystem_mcp_server.py  # FastMCP server, 8 filesystem tools
│   └── config.py             # Env-driven configuration
├── workspace/                # Sandboxed sample files used by the tools
├── docs/wiki-draft/          # Draft wiki pages (see below)
├── requirements.txt
├── .env.example
└── CHANGELOG.md

Documentation

A draft GitHub Wiki lives in docs/wiki-draft/ (Home, Getting Started, Architecture, FAQ) — see that folder's note on how to publish it.

Changelog

See CHANGELOG.md.

Security

No committed secrets were found in this repository's tracked files or git history. .env is correctly git-ignored and only .env.example (with a placeholder key) is tracked.

License

MIT

Contributors

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