File Server MCP

File Server MCP

A hybrid REST + MCP file server for managing files with large file streaming support, enabling AI agents and web frontends to perform file operations via MCP tools and REST APIs.

Category
Visit Server

README

File Server MCP

A hybrid REST + MCP file server built with Python, FastMCP, and UV. Provides both REST API endpoints (for web frontends) and MCP tools (for AI agents) to manage files with support for large file streaming.

Features

  • Hybrid Interface: Both REST API and MCP tools for maximum flexibility
  • Large File Support: Streaming downloads with no size limits
  • Type Safety: Full Pydantic validation across all interfaces
  • Security: Path traversal prevention and configurable size limits
  • Async I/O: Efficient file operations with aiofiles
  • Easy Configuration: Environment-based settings

Architecture

MCP Tools (for AI Agents)

  • list_files_tool: List all files with metadata
  • get_file_info_tool: Get detailed file information
  • get_file_download_link_tool: Get REST API download URL for large files
  • create_file_tool: Create new files (text or base64 binary, 100MB limit)
  • delete_file_tool: Delete files
  • replace_file_tool: Replace file content (text or base64 binary, 100MB limit)

REST API Endpoints

  • GET /api/files: List all files
  • GET /api/files/{filename}: Get file metadata
  • GET /api/files/{filename}/download: Download file (streaming, no size limit)
  • DELETE /api/files/{filename}: Delete file

Utility Endpoints

  • GET /: API information
  • GET /health: Health check
  • GET /docs: OpenAPI documentation

Installation

Prerequisites

  • Python 3.11 or higher
  • UV package manager

Install UV

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

Setup Project

# Clone or navigate to the project directory
cd file-server-mcp

# Install dependencies
uv sync

# Optional: Install development dependencies
uv sync --extra dev

Configuration

Create a .env file in the project root (see .env.example):

FILE_SERVER_FILE_STORAGE_DIR=./file_storage
FILE_SERVER_MAX_UPLOAD_SIZE=104857600  # 100MB
FILE_SERVER_CHUNK_SIZE=1048576         # 1MB

Environment Variables

  • FILE_SERVER_FILE_STORAGE_DIR: Directory for file storage (default: ./file_storage)
  • FILE_SERVER_MAX_UPLOAD_SIZE: Maximum file size for uploads via MCP tools in bytes (default: 100MB)
  • FILE_SERVER_CHUNK_SIZE: Chunk size for streaming downloads in bytes (default: 1MB)

Usage

Start the Server

# Run with UV
uv run python -m file_server_mcp

# Or with uvicorn directly
uv run uvicorn file_server_mcp.server:app --host 0.0.0.0 --port 8000 --reload

The server will start on http://localhost:8000.

REST API Examples

# List all files
curl http://localhost:8000/api/files

# Get file info
curl http://localhost:8000/api/files/example.txt

# Download file
curl http://localhost:8000/api/files/example.txt/download -o downloaded.txt

# Delete file
curl -X DELETE http://localhost:8000/api/files/example.txt

# Health check
curl http://localhost:8000/health

# View OpenAPI docs
# Open http://localhost:8000/docs in your browser

MCP Integration

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "file-server": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/file-server-mcp",
        "run",
        "python",
        "-m",
        "file_server_mcp"
      ],
      "env": {
        "FILE_SERVER_FILE_STORAGE_DIR": "/path/to/storage"
      }
    }
  }
}

MCP Inspector

# Install MCP Inspector
npm install -g @modelcontextprotocol/inspector

# Run the server with inspector
mcp-inspector uv --directory /path/to/file-server-mcp run python -m file_server_mcp

Using MCP Tools

Once configured with Claude Desktop, you can ask Claude to:

"List all files in the file server"
"Get information about report.pdf"
"Create a new file called notes.txt with content 'Hello World'"
"Get a download link for large-video.mp4"
"Delete old-file.txt"
"Replace the content of config.json"

Development

Run Tests

# Run all tests
uv run pytest

# Run with coverage
uv run pytest --cov=file_server_mcp --cov-report=html

# Run specific test file
uv run pytest tests/test_storage.py

# Run with verbose output
uv run pytest -v

Project Structure

file-server-mcp/
├── .python-version          # Python version
├── pyproject.toml           # UV configuration and dependencies
├── uv.lock                  # Auto-generated lockfile
├── .env.example             # Environment variable template
├── README.md                # This file
├── src/
│   └── file_server_mcp/
│       ├── __init__.py      # Package initialization
│       ├── __main__.py      # Entry point
│       ├── config.py        # Environment configuration
│       ├── models.py        # Pydantic schemas
│       ├── storage.py       # File operations
│       ├── rest_routes.py   # FastAPI REST endpoints
│       ├── mcp_tools.py     # MCP tool definitions
│       └── server.py        # Main hybrid server
└── tests/
    ├── __init__.py
    ├── conftest.py          # Pytest fixtures
    ├── test_storage.py      # Storage layer tests
    ├── test_rest_routes.py  # REST API tests
    └── test_mcp_tools.py    # MCP tools tests

Large File Handling

Strategy

  • REST Downloads: Use streaming with aiofiles and StreamingResponse (no size limit)
  • MCP Tools:
    • Direct content transfer limited to 100MB (configurable)
    • For larger files, use get_file_download_link_tool to get REST URL
    • AI agents can fetch large files via the REST endpoint

Example: Handling Large PDFs

# For small files (< 100MB), use MCP tool directly
create_file_tool(filename="small.pdf", content=base64_content, encoding="base64")

# For large files, use download link
link = get_file_download_link_tool(filename="large.pdf")
# Returns: {"filename": "large.pdf", "download_url": "/api/files/large.pdf/download"}
# Fetch via REST API using the download_url

Security

Path Traversal Prevention

All filenames are sanitized to prevent directory traversal attacks:

# Input: "../../etc/passwd"
# Sanitized to: "passwd"
# Saved in: <storage_dir>/passwd

Authentication

This is a prototype server without authentication. For production use:

  • Add authentication middleware (e.g., API keys, OAuth)
  • Implement authorization for file operations
  • Use HTTPS for transport security
  • Consider rate limiting

Troubleshooting

Server won't start

# Check Python version
python --version  # Should be 3.11+

# Reinstall dependencies
uv sync --reinstall

# Check storage directory permissions
ls -la ./file_storage

MCP tools not appearing in Claude Desktop

  1. Check claude_desktop_config.json is valid JSON
  2. Verify the command path is absolute
  3. Check Claude Desktop logs (Help → View Logs)
  4. Restart Claude Desktop after config changes

Tests failing

# Clear pytest cache
rm -rf .pytest_cache

# Run tests with verbose output
uv run pytest -vv

# Run a single test
uv run pytest tests/test_storage.py::test_sanitize_filename -v

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured