EventOps: an MCP server + AI CLI for event operations

EventOps turns years of messy event ticketing exports into something an AI tool can actually reason about. It exposes MQIS event and membership data (Humanitix exports) to any Model Context Protocol client (Claude Desktop, Claude Code, Cursor) as a set of deterministic tools, and ships a small AI-enabled command line tool that writes ops reports and consent-aware emails on top of those same tools.

I built it to give my AI tools project-specific context instead of generic answers. That is the whole idea: the AI can call get_event_metrics("Saazish") and get the exact figure, rather than guessing.

Design principle: deterministic tools, AI for narrative

Every number comes from pandas, never from a language model. The MCP tools and the CLI both call into one EventData object (data_loader.py) that does the counting. The AI only decides which tool to call and how to phrase the result. It cannot invent a revenue figure, because it never computes one. This is the single most important property of the project.

What it does (real numbers from my own events)

Across 8 datasets: 3,690 orders, 4,690 valid tickets, ~2,770 unique people, and about A$61.4k in revenue to MQIS (net of platform fees; ~A$62.1k gross ticket sales). 238 people attended 2 or more distinct events, 69 of whom also held a membership.

Three money columns exist in the source data and they are not the same thing: Ticket sales (gross face value), Paid (what the customer paid including Humanitix fees), and Your earnings (what MQIS actually received). EventOps reports Your earnings as "revenue to MQIS" so the headline number is honest.

Tools exposed over MCP (server.py)

Tool	What it returns
list_events	Every dataset with its key and label
get_event_metrics(event_name)	Orders, tickets, net + gross revenue, paid/free/refunded split, online vs manual channel
get_sales_curve(event_name)	Sales over time, plus % of tickets sold in the final 48h / 7 days
lookup_attendee(query)	One person's full history across all files, by email or name
find_loyal_members(min_events=2)	Cross-event regulars, and how many are members
build_marketing_segment(target)	A consent-aware contact list (opt-in = Yes only)
portfolio_summary	Totals across everything

AI CLI (cli.py)

python cli.py summary
python cli.py report      --event "Saazish"
python cli.py compare     --e1 "Raaz" --e2 "Saazish"
python cli.py draft-email --type promo --target non-members --event "Gulaal"
python cli.py attendee    --query "someone@example.com"

Add --raw to any command to print the underlying JSON facts instead of the AI-written version. This is a good way to prove the tools are exact: the AI output only ever restates the --raw numbers.

The draft-email command only ever includes people whose Marketing opt-in is Yes, so it will not draft to anyone who did not consent.

Setup

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# 1. Sample data (safe, synthetic) so it runs out of the box:
python gen_sample_data.py

# 2. Or use real data: drop your Humanitix CSV exports into ./data/
#    (that folder is git-ignored and never committed)

# 3. For the CLI, set your key:
cp .env.example .env   # then edit, or just:
export ANTHROPIC_API_KEY=sk-ant-...

If ./data is empty, EventOps automatically falls back to ./sample_data, so a fresh clone works immediately.

Registering the MCP server with a client

Claude Desktop / Claude Code (claude_desktop_config.json):

{
  "mcpServers": {
    "eventops": {
      "command": "python",
      "args": ["/absolute/path/to/event-ops-mcp/server.py"]
    }
  }
}

Cursor (.cursor/mcp.json): same shape. Restart the client, then ask it things like "Using eventops, compare Raaz and Saazish" or "Who are our most loyal attendees?" and it will call the tools.

Data privacy

Real exports contain personal information (names, emails, phone numbers). They live in ./data/, which is git-ignored. Only the synthetic ./sample_data/ is ever committed. Do not commit real attendee data.

Files

event-ops-mcp/
├── data/                # real Humanitix CSVs (git-ignored, you provide)
├── sample_data/         # synthetic CSVs, safe to commit
├── data_loader.py       # cleaning + all deterministic analytics
├── server.py            # FastMCP server exposing the tools
├── cli.py               # AI-enabled CLI (Anthropic API)
├── gen_sample_data.py   # builds the synthetic dataset
├── requirements.txt
├── .env.example
└── .gitignore