VeloceMCP
A read-only MCP server that enables restaurant staff to query Veloce POS data using natural language, supporting sales summaries, payment breakdowns, and weekly reports.
README
Veloce MCP Server
A read-only Model Context Protocol (MCP) server that lets restaurant staff query their Veloce POS data in plain language through an MCP client — Claude Desktop, the MCP Inspector, Claude Code, etc.
Built with the FastMCP Python SDK. Every Veloce endpoint it touches is read-only — there are no tools that can change or delete data.
About this repository. This is a generalized, portfolio version of a server originally built for a multi-location restaurant client. Credentials, real location names, and all business data have been removed; the location names here (
Downtown,Midtown, …) are placeholders you can rename. Veloce is a third-party POS platform and is not affiliated with this project.
What it can answer
| Tool | Answers questions like |
|---|---|
list_locations |
"Which locations can I ask about?" |
get_sales_summary |
"What were Downtown's total sales last Saturday?" |
get_daily_sales |
"Show me Uptown day-by-day for last week." |
get_payment_breakdown |
"How much came in via Uber vs cash at Midtown yesterday?" |
get_top_products |
"What were Eastside's 10 best sellers in May?" |
get_category_sales |
"Food vs beverage split at Westside last month?" |
get_hourly_sales |
"What's the busiest hour at Downtown on Sundays?" |
get_service_mode_sales |
"Dine-in vs delivery at Midtown last week?" |
get_employee_sales |
"Who were the top servers at Uptown in April?" |
get_refunds_and_voids |
"Any unusual refunds at Eastside yesterday?" |
Weekly reports
| Tool | Produces |
|---|---|
get_upsell_report |
Per-server upsell performance: beverages + food upgrades + sides as a % of each server's sales. |
get_lto_report |
Per-server Limited Time Offer sales vs. the tracked LTO list. |
get_avm_report |
Average Meal Value (net sales ÷ meal count) with a daily breakdown. |
LTO items — managed as data, not code
The Limited Time Offer list lives in lto_items.json and is matched as case-insensitive substrings. When a promo changes, update it by conversation instead of combing the item list:
| Tool | Use |
|---|---|
get_lto_items / set_lto_items |
View or replace the tracked LTO list. |
search_menu_items |
Find sold items by keyword (e.g. every "blueberry" item). |
find_new_items |
Surface items that started selling recently but weren't selling in an earlier baseline period — i.e. likely new LTOs. |
Typical flow: "What new items showed up this month at Downtown?" → find_new_items lists candidates → "Track those as LTOs" → set_lto_items saves them → get_lto_report uses them automatically.
It also exposes the location list as a resource (veloce://locations) and a daily_briefing prompt that produces an end-of-day manager summary.
Setup
-
Install dependencies (Python 3.10+):
cd veloce-mcp-server python3 -m venv .venv && source .venv/bin/activate pip install -r requirements.txt -
Configure your locations. Edit
_LOCATION_KEYSinconfig.pyto match your locations, then add credentials. Each location is a separate Veloce login, so each needs its own email/password.cp .env.example .envOpen
.envand fill in the values. Never commit.env— only.env.example(which holds no secrets) is safe to share.
Test it locally with the MCP Inspector
mcp dev server.py
This opens the browser-based Inspector. Click Connect, then try a tool: pick get_sales_summary, enter a location (e.g. Downtown) and a date, and run it. This is the fastest way to confirm credentials and the connection work before wiring it into a client.
Connect it to Claude Desktop
Add this to your Claude Desktop config file
(~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"veloce": {
"command": "/absolute/path/to/veloce-mcp-server/.venv/bin/python",
"args": ["/absolute/path/to/veloce-mcp-server/server.py"]
}
}
}
Use absolute paths, then restart Claude Desktop. The Veloce tools will appear and you can ask questions like "What were Downtown's sales last Saturday and how did people pay?"
Notes & conventions
- Dates are
YYYY-MM-DD. Where a range is optional it defaults to today. - Tokens are cached in memory per location and refreshed automatically on a 401, so the server logs in once per location per session.
- Locations are matched case-insensitively and by prefix (
down→Downtown). - The server only loads locations that have both an email and password set, so it still runs with a subset configured.
Architecture
server.py— defines the MCP tools, resource, and prompt (FastMCP).veloce_client.py— per-location auth, in-memory token caching, automatic re-auth on 401, and a generic authenticated GET helper. All calls are read-only.config.py— loads per-location credentials from the environment and resolves friendly location names.lto_items.json— the editable Limited Time Offer list.
Extending it
The Veloce API exposes many read-only endpoints; this server wraps the most useful ~10. To add another (e.g. /sales/taxes, /transactions/giftCards, /sales/discounts), add a new @mcp.tool() function in server.py that calls vc.api_get(location, "/the/path", {params}). See the Veloce API documentation for the full endpoint list.
License
MIT — see LICENSE.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.