Personal File Search Server

A locally-running MCP (Model Context Protocol) server that turns your personal files into a searchable knowledge base. Ask questions in plain English and get answers pulled directly from your own documents — no cloud storage, no data leaving your machine.

Built to demonstrate RAG (Retrieval-Augmented Generation), vector search, and MCP server development using the official Anthropic SDK.

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What it actually does

Drop any .txt, .md, or .pdf files into the data/ folder. The indexer reads them, breaks them into chunks, converts each chunk into a numerical vector using a local AI model, and stores everything in ChromaDB — a local vector database.

Once indexed, you can connect the server to Claude Desktop and ask things like:

• "What do my notes say about machine learning?"
• "Summarise everything I have on Project Apollo"
• "Find my meeting notes from last quarter"
• "Read the full content of report.pdf"

The search works by meaning, not keywords. So searching for "AI impacts employment" will find a document that says "artificial intelligence is changing the job market" — even though none of those exact words match.

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How it works under the hood

Phase 1 — Indexing (run once)

Your files (.txt / .md / .pdf)
    ↓
LangChain loaders      read raw text from disk
    ↓
Text splitter          cut into ~500-character chunks with overlap
    ↓
Embedding model        convert each chunk into a 384-dim vector (runs locally)
    ↓
ChromaDB               store vectors + text + metadata on disk

Phase 2 — Search (every query)

Your question (plain English)
    ↓
Embedding model        convert query into a vector
    ↓
ChromaDB               find the most similar stored vectors (cosine similarity)
    ↓
Gemini 2.5 Flash       optionally synthesise results into a clean summary
    ↓
Answer                 returned to Claude Desktop via MCP protocol

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The 4 tools exposed via MCP

Tool	What it does
search_files	Semantic search — returns the most relevant chunks with relevance scores
read_file	Reads the complete content of a specific file
summarise_topic	Searches files AND synthesises results into a Gemini-written summary
list_files	Lists all files currently indexed and available to search

Claude Desktop automatically decides which tool to call based on the conversation. You just ask naturally.

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Tech stack

Layer	Tool	Why
MCP server	mcp[cli] (Anthropic SDK)	Official protocol — works with any MCP-compatible LLM
RAG framework	LangChain	Document loaders, text splitters, vector store interface
Vector database	ChromaDB	Local, no server needed, persists to disk
Embedding model	sentence-transformers (all-MiniLM-L6-v2)	Runs 100% locally, no API cost, 90MB one-time download
LLM (summarise)	Gemini 2.5 Flash	Free tier, used only for the summarise tool
File watcher	watchdog	Auto re-indexes when files change
Config	python-dotenv	Keeps API keys out of source code

Everything runs locally except the Gemini summarise call.

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Project structure

file-search-mcp/
├── src/
│   ├── __init__.py      marks src/ as a Python package
│   ├── indexer.py       RAG indexing pipeline — load, chunk, embed, store
│   ├── retriever.py     semantic search against ChromaDB
│   ├── server.py        MCP server — defines and exposes the 4 tools
│   └── watcher.py       file system watcher for auto re-indexing
├── data/                put your .txt / .md / .pdf files here
├── chroma_db/           ChromaDB database (auto-created, do not commit)
├── run_indexer.py       run this to build or update the search index
├── test_search.py       test all tools locally without Claude Desktop
├── requirements.txt     Python dependencies
├── .env         copy to .env and fill in your API keys
├── .gitignore
└── README.md

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Getting started

Prerequisites

• macOS, Linux, or Windows (WSL recommended on Windows)
• Python 3.11 or higher
• An internet connection (for first-time model download and Gemini calls)

Check your Python version:

python3 --version

If it shows 3.9 or lower, install 3.11 via Homebrew (macOS):

brew install [email protected]

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Step 1 — Clone the repository

git clone https://github.com/rafiqul713/file-search-mcp.git
cd file-search-mcp

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Step 2 — Create a virtual environment

A virtual environment is an isolated Python installation just for this project. It keeps dependencies completely separate from your system Python and from other projects — no version conflicts, no broken installs.

# Create the virtual environment in a folder called .venv
python3.11 -m venv .venv

Now activate it. You must do this every time you open a new terminal:

# macOS / Linux
source .venv/bin/activate

# Windows (Command Prompt)
.venv\Scripts\activate.bat

# Windows (PowerShell)
.venv\Scripts\Activate.ps1

When the virtual environment is active, your terminal prompt will show (.venv) at the start:

(.venv) rafiqul@Mac file-search-mcp %

That (.venv) tells you that any python or pip command runs inside the isolated environment, not your system Python.

To deactivate (when you're done working):

deactivate

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Step 3 — Install dependencies

With the virtual environment active:

# Upgrade pip first (avoids some install issues)
pip install --upgrade pip

# Install all dependencies
pip install --timeout 120 -r requirements.txt

The first time this runs, it will download the sentence-transformers embedding model (~90MB from HuggingFace). This only happens once — it's cached locally after that.

If any package times out, install it alone with a longer timeout:

pip install --timeout 300 sentence-transformers

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Step 4 — Get your free API keys

You only need one API key for the summarise feature. Everything else (search, read, list) runs completely locally.

Google Gemini (free)

1. Go to aistudio.google.com/apikey
2. Sign in with any Google account
3. Click Create API key
4. Copy the key — it starts with AIza...

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Step 5 — Configure your environment

Open `.env` in your editor:

```bash
nano .env        # terminal editor
# or
code .env        # VS Code

Fill in your values:

GOOGLE_API_KEY=AIzaSy...your_key_here...
FILES_DIR=./data
CHROMA_DIR=./chroma_db
COLLECTION_NAME=my_files

Save and close. Your .env is listed in .gitignore so it will never be accidentally committed to GitHub.

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Step 6 — Add your files

Put any .txt, .md, or .pdf files you want to search into the data/ folder:

# The folder is created automatically, but you can make it manually
mkdir -p data

# Copy some files in
cp ~/Documents/notes.md data/
cp ~/Downloads/report.pdf data/

Or create a test file to try right away:

cat > data/sample.md << 'EOF'
# My Notes

## LangGraph
LangGraph is a library for building stateful multi-agent AI applications.
It uses a graph approach where nodes are functions and edges define flow.

## RAG
Retrieval Augmented Generation combines semantic search with LLM generation.
Instead of relying on training data, the LLM gets relevant context injected.

## MCP
Model Context Protocol is an open standard by Anthropic.
It lets LLMs connect to external tools in a standardised way.
EOF

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Step 7 — Build the index

python run_indexer.py

You will see output like:

=======================================================
  Personal File Search — Indexer
=======================================================
Scanning data/ directory for .txt, .md, .pdf files...
(First run downloads the embedding model ~90MB)

[Indexer] Loading embedding model (first run downloads ~90MB)...
[Indexer] Indexing: sample.md
[Indexer] Done — 1 file(s) indexed, 0 unchanged.

Running it again later only re-indexes new or changed files — unchanged files are skipped.

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Step 8 — Test that everything works

Before setting up Claude Desktop, verify search is working locally:

python test_search.py

This tests all 4 tools directly in your terminal. You should see your indexed files listed and get real search results back.

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Step 9 — Connect to Claude Desktop

Download Claude Desktop from claude.ai/download if you haven't already.

Open the Claude Desktop configuration file:

# macOS
nano ~/Library/Application\ Support/Claude/claude_desktop_config.json

Add this configuration (replace the paths with your actual project location):

{
  "mcpServers": {
    "file-search": {
      "command": "/path/to/file-search-mcp/.venv/bin/python",
      "args": [
        "/path/to/file-search-mcp/src/server.py"
      ],
      "env": {
        "GOOGLE_API_KEY": "your_gemini_key_here",
        "FILES_DIR": "/path/to/file-search-mcp/data",
        "CHROMA_DIR": "/path/to/file-search-mcp/chroma_db",
        "COLLECTION_NAME": "my_files"
      }
    }
  }
}

To find your exact project path:

pwd

Restart Claude Desktop. You should see a hammer icon (🔨) in the chat input area — that confirms your MCP tools are connected.

Now try asking Claude:

"What files do you have access to?"

"Search my files for anything about LangGraph"

"Summarise what my notes say about RAG"

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Keeping the index up to date

Option A — Manual (run after adding or editing files):

source .venv/bin/activate
python run_indexer.py

Option B — Automatic watcher (runs in the background, re-indexes on every save):

source .venv/bin/activate
python -c "from src.watcher import start_watcher; start_watcher()"

Leave this running in a terminal while you work. Every time you save a file to data/, it re-indexes automatically within seconds.

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Common issues

(.venv) not showing in terminal

The virtual environment isn't active. Run:

source .venv/bin/activate

ModuleNotFoundError: No module named 'src'

You're running the script from the wrong directory. Always run commands from the project root (where run_indexer.py lives):

cd /path/to/file-search-mcp
python run_indexer.py

GOOGLE_API_KEY not found

Your .env file is missing or in the wrong place. It must be in the project root (same folder as run_indexer.py). Double-check:

ls -la | grep .env
cat .env

Gemini rate limit error in summarise

The free tier allows ~15 requests per minute. Wait a moment and try again. The search_files and read_file tools don't call Gemini, so they always work instantly.

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Requirements

• Python 3.11+
• macOS, Linux, or Windows (WSL)
• ~500MB disk space (ChromaDB index + embedding model cache)
• Internet connection for first setup and Gemini summarise calls

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License

MIT — do whatever you want with this.

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