File Reader

A fully local, free AI-powered file reader that uses MCP (Model Context Protocol) to connect a Python tool server with a local LLM (Ollama + Mistral). No API keys, no cloud, no cost — runs entirely on your machine.

🧾 Features

• MCP Server with custom tools (list_files, read_file)
• Local LLM integration via Ollama (Mistral / Llama3.2)
• Automatic file discovery and content reading
• AI-powered summarization of file contents
• Path traversal protection (security built-in)
• Zero cost — no API keys or subscriptions required

🛠️ Tech Stack

Technology	Description
Python	Core language for server and client
MCP	Model Context Protocol (tool server)
Ollama	Local LLM runtime (free, offline)
Mistral	Local AI model for summarization
asyncio	Async communication between client/server
requests	HTTP calls to Ollama API

📦 Installation

git clone https://github.com/JaneKarunyaJ/MCP-File-Reader.git
cd MCP-File-Reader
pip install mcp requests

Install Ollama from https://ollama.com, then pull the model:

ollama pull mistral

🚀 Usage

Make sure Ollama is running (it starts automatically after installation), then:

python client.py

The client will:

1. Launch the MCP server as a subprocess
2. Call list_files to discover files in my_files/
3. Call read_file for each file found
4. Send the real content to Mistral for summarization

📁 Project Structure

MCP-File-Reader/
│
├── server.py          # MCP server — exposes list_files and read_file tools
├── client.py          # MCP client — calls tools and queries Ollama
├── requirements.txt   # Python dependencies
└── my_files/          # Folder the AI is allowed to read
    ├── project_ideas.txt
    └── wishlist.txt

🔐 Security

• The MCP server only allows reads from the my_files/ directory
• Path traversal attacks (e.g. ../../etc/passwd) are automatically blocked
• No data leaves your machine — fully offline after setup

🧠 How It Works

client.py
   │
   ├── Step 1: Calls MCP tool → list_files()
   │              ↓
   │         Returns filenames from my_files/
   │
   ├── Step 2: Calls MCP tool → read_file(filename)
   │              ↓
   │         Returns actual file contents
   │
   └── Step 3: Sends real content to Ollama (Mistral)
                  ↓
             Returns AI summary

➕ Extending the Project

• Add your own files: Drop any .txt file into my_files/ and run again
• Add new tools: Add a new tool handler in server.py (e.g. search_in_file, write_file)
• Change the question: Edit user_question in client.py to ask anything about your files
• Swap the model: Change MODEL = "mistral" in client.py to any model you have pulled in Ollama

📋 Requirements

• Python 3.9+
• Ollama installed (ollama.com)
• Mistral model pulled (ollama pull mistral)
• mcp and requests Python packages