Bakery Data

Provides access to bakery POS data stored in SQLite via an MCP server with querying and analytics tools.

python

0

GitHub Stars

python

Language

4 months ago

First Indexed

3 weeks ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "t2hnd-bakery_data_mcp": {
      "command": "python",
      "args": [
        "-m",
        "bakery_data_mcp.server"
      ]
    }
  }
}

You deploy a Python-based MCP server that exposes bakery POS data stored in SQLite, enabling clients to query transactions, products, departments, and sales analytics through structured MCP tools. This server makes it easy to import CSV data into SQLite and then access it with flexible queries and aggregated summaries.

How to use

You run the MCP server locally and interact with it through an MCP client. Start the server, then issue calls to the provided tools to query transactions, products, departments, and sales analytics. You can filter by date ranges, product attributes, department, and more, and you can request aggregated results such as top-selling products or sales summaries by group.

How to install

pip install mcp

Or install in development mode:

pip install -e .

Import the data into SQLite to create the database and load the CSV data. Run the import script to build bakery_data.db and populate the tables from the Data directory. It will also create indexes and display database statistics.

python import_data.py

Configure the MCP server in your environment. You will run the server locally using Python and point your client to the standard input/output interface exposed by the process.

Start the MCP server directly for testing during development.

python -m bakery_data_mcp.server

Additional notes

The project stores data in a SQLite database and exposes several tools that let you query and summarize bakery sales data. The server’s runtime is designed for local development and testing via stdio.

Project structure and runtime entry points include a data import script, a Python MCP server module, and a schema that defines the database layout.

Available tools

query_transactions

Query POS transaction data with filters such as start_date, end_date, product_code, product_name, payment_method, amount range, and limit.

query_products

Query product master data with filters for plu_code, product_name, department_id, price range, tag, include_tags, and limit.

query_departments

Query department master data with optional department_id or department_name searches.

sales_summary

Get aggregated sales statistics with date range, group_by options (product, department, payment_method, date, month), department filter, and limit.

top_products

Retrieve top-selling products by date range, department, and metric (quantity or revenue) with a limit.

execute_sql

Execute custom SQL queries on the database with optional parameters. Use read-only queries when possible.

get_schema

Get database schema information including table structures and row counts.