mcp-sqlite-chat
Allows querying and managing a SQLite database using natural language, with tools for listing tables, describing schemas, running SELECT queries, inserting users and orders.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-sqlite-chatwhat is the total revenue?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mcp-sqlite-chat
A local AI chat interface that lets you query and manage a SQLite database using plain English.
Built with MCP (Model Context Protocol) + Groq (free LLM). You type natural language questions; the LLM decides which database tools to call; results come back as clean, readable answers.
How It Works
You type a question
│
▼
Groq LLM (llama-3.1-8b-instant)
decides which tools to call
│
▼
MCP Client ──────────> MCP Server (server.py)
│
▼
Runs SQL on users.db
<────────── Returns result
│
▼
Groq formats a human-readable answer
│
▼
"There are 2 users from London."The LLM never touches the database directly — all SQL runs through the MCP server.
Related MCP server: sqlite-mcp
Project Structure
mcp-sqlite-chat/
server.py — MCP server: exposes the SQLite DB as tools and resources
client.py — Chat interface: connects to the MCP server and Groq
init_db.py — Creates users.db with sample data
db_inspect.py — Utility to inspect the database directly
users.db — SQLite database (generated after running init_db.py)
requirements.txt
.env.exampleSetup
1. Create and activate a virtual environment
From your project root (e.g. /home/me/my-project/):
python3 -m venv venv
source venv/bin/activate2. Install dependencies
pip install -r requirements.txt3. Configure your Groq API key
Copy the example env file and fill in your key:
cp .env.example .envEdit .env:
GROQ_API_KEY=your-groq-api-key-hereGet a free key at https://console.groq.com
4. Initialize the database
python init_db.pyExpected output:
Database created at: .../users.db
- 8 users inserted
- 10 orders insertedRunning
# Make sure the venv is active (from the project root)
source venv/bin/activate
# Start the chat
python client.pyYou will see:
Connecting to SQLite MCP server...
Connected. Tools available: ['list_tables', 'describe_table', 'run_query', 'insert_user', 'insert_order']
--------------------------------------------------
SQLite Chat — type your question or 'quit' to exit
--------------------------------------------------
You:Example Queries
Explore the schema
You: what tables exist?
You: describe the users table
You: what columns does orders have?Query users
You: show all users
You: how many users are from London?
You: who is the youngest user?
You: list users older than 30Query orders
You: show all orders
You: what did Bob order?
You: show all orders over $300
You: what is the total revenue?Join queries
You: who spent the most money in total?
You: show each user and how much they spent
You: which city's users spend the most?Insert data
You: add user Sara, sara@gmail.com, age 25, from Paris
You: add an order for alice@example.com for a Monitor costing 349.99Type quit, exit, or q to leave. Ctrl+C also works.
MCP Tools
Tool | Description |
| Lists all tables in the database |
| Shows columns and types for a table |
| Runs a SELECT query and returns rows |
| Adds a new user |
| Adds an order for an existing user |
run_query is read-only — only SELECT statements are allowed. All writes go through insert_user and insert_order, which use parameterized queries.
Tech Stack
MCP (Model Context Protocol) — tool/resource protocol connecting the client to the server
Groq — free, fast LLM inference (
llama-3.1-8b-instant)FastMCP — Python framework for building MCP servers
SQLite — local database, no server needed
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