notes-rag-mcp

Table of Contents

• Overview

• Features

• Architecture

• Tech Stack

• Getting Started

• Configuration

• MCP Tools

• Connecting to MCP Clients

• Project Structure

• License

Related MCP server: RAGandLLM-MCP

Overview

Notes RAG MCP Server is a Model Context Protocol server that turns a personal note collection into a knowledge base an AI agent can query. Notes are persisted in PostgreSQL (source of truth), their embeddings stored in ChromaDB, and answers generated by Google Gemini through a declarative LangChain LCEL chain.

Any MCP-compatible client — Claude Desktop, Claude Code, the MCP Inspector, or a custom agent — can call the six exposed tools to add, search, ask, summarize, list, and delete notes.

Features

• Semantic search — vector similarity search via ChromaDB returns the most relevant notes for any query

• RAG Q&A — a full LCEL pipeline (retriever | prompt | llm | parser) grounds answers strictly in stored notes, never hallucinating beyond context

• Consistent dual store — every write and delete keeps PostgreSQL and ChromaDB in sync automatically

• Note summarization — dedicated LCEL chain condenses any note to 2–3 sentences on demand

• MCP resource — notes are also exposed as notes://<id> resources for direct content access

• Lazy initialization — Gemini and ChromaDB clients are created only on first use; importing the module requires no API key

• Idempotent setup — the database schema is applied at startup with IF NOT EXISTS; safe to restart at any time

Architecture

MCP Client (Claude Desktop / Inspector / Claude Code)
        │  stdio transport
        ▼
┌─────────────────────────────┐
│       server.py             │
│  FastMCP — 6 tools +        │
│  notes://{id} resource      │
└────────┬────────────────────┘
         │
    ┌────┴────┐
    ▼         ▼
┌────────┐  ┌──────────────────────────────────────┐
│  db.py │  │           rag_chain.py               │
│        │  │  GoogleGenerativeAIEmbeddings         │
│ psycopg│  │  ChatGoogleGenerativeAI (Gemini)      │
│   3    │  │  Chroma (langchain-chroma)            │
│        │  │                                      │
│ Source │  │  LCEL: retriever | prompt | llm |    │
│ of     │  │         StrOutputParser              │
│ truth  │  └──────────────────────────────────────┘
└───┬────┘           │
    │                │
    ▼                ▼
┌──────────┐   ┌───────────┐
│PostgreSQL│   │ ChromaDB  │
│ (notes   │   │ (vector   │
│  table)  │   │  index)   │
└──────────┘   └───────────┘

RAG question flow (ask tool):

{"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt | llm | StrOutputParser()

The ChromaDB retriever fetches the most similar notes → format_docs merges them into a context block → Gemini answers only from that context.

Tech Stack

Getting Started

Prerequisites

• Python ≥ 3.12

• PostgreSQL running locally (e.g. postgresql@15 via Homebrew)

• A Google AI Studio API key

Installation

# 1. Clone the repository
git clone https://github.com/konradxmalinowski/project-rag-mcp.git
cd project-rag-mcp

# 2. Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate

# 3. Install dependencies
pip install -e .

# 4. Create the database
createdb notes_mcp

# 5. Configure environment variables
cp .env.example .env
# Edit .env — set GEMINI_API_KEY and verify DATABASE_URL

The notes table is created automatically on first server startup. You can also apply the schema manually:

psql -d notes_mcp -f schema.sql

Configuration

Copy .env.example to .env and fill in the required values. Never commit .env.

MCP Tools

MCP resource: notes://{note_id} — exposes raw note content for direct read access.

Connecting to MCP Clients

MCP Inspector (browser UI — easiest for testing)

mcp dev server.py

Open the Inspector in your browser. In the Tools tab:

1. add_note("Python GIL", "The GIL is a mutex that protects access to Python objects...")

2. Add 2–3 more notes on different topics.

3. search_notes("what is the GIL") → the GIL note should rank first.

4. ask("explain the GIL based on my notes") → grounded answer citing note titles.

5. summarize_note(1), then delete_note(1) → verify PostgreSQL ↔ ChromaDB consistency.

Claude Desktop

Add the following entry to your Claude Desktop MCP config (claude_desktop_config.json):

{
  "mcpServers": {
    "notes-rag": {
      "command": "/absolute/path/to/.venv/bin/python",
      "args": ["/absolute/path/to/server.py"]
    }
  }
}

Claude Code (CLI)

claude mcp add notes-rag /absolute/path/to/.venv/bin/python -- /absolute/path/to/server.py

Project Structure

project-rag-mcp/
├── server.py        # FastMCP entry point — 6 tools + notes resource
├── rag_chain.py     # LangChain LCEL: Gemini + ChromaDB + RAG chain
├── db.py            # PostgreSQL data layer (psycopg3)
├── schema.sql       # notes table DDL
├── pyproject.toml   # project metadata and dependencies
├── .env.example     # environment variable template
└── LICENSE

License

MIT © 2025 Konrad Malinowski