Acme Operations Assistant (MCP demo)

A client-ready demo: an AI operations assistant for customer support and account managers, built on the Model Context Protocol.

Stack: MCP server → SQLite business data → OpenAI → Streamlit UI

Why this is a “real” project

• Business domain — customers, orders, support tickets (not hello world math)

• Persistent data — seeded SQLite database you can reset and extend

• Transparent tool calls — the UI shows exactly which MCP tools ran and what they returned

• Portable integration — the same MCP server works in Cursor, custom apps, or OpenAI Agents SDK

Related MCP server: Gestao-MCP

Quick start (client demo)

cd /apps/tmp/mcp-servers
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[demo]"

export OPENAI_API_KEY=sk-...
streamlit run demo_app.py

Open http://localhost:8501 and use the sidebar scenario buttons.

Full presenter script: DEMO.md

Project layout

MCP tools

Commands

# UI demo (recommended for clients)
streamlit run demo_app.py

# MCP smoke test — no OpenAI key
python client.py

# CLI assistant
python openai_chat.py "Account summary for CUST-1003"

# Reset demo database
python scripts/reset_demo_data.py

Connect to OpenAI (details)

The MCP server uses stdio. Your app must spawn it (as demo_app.py and assistant.py do).

• Chat Completions bridge: assistant.py / openai_chat.py

• Agents SDK: openai_agent.py — pip install -e ".[agents]"

• Responses API (remote URL): deploy server over HTTP; see OpenAI MCP guide

Cursor / Claude Desktop

{
  "mcpServers": {
    "acme-operations": {
      "command": "/apps/tmp/mcp-servers/.venv/bin/python",
      "args": ["/apps/tmp/mcp-servers/server.py"]
    }
  }
}

Next steps for production

1. Replace SQLite with CRM/ERP APIs behind the same tool names

2. Add auth (OAuth) per user and row-level access on tools

3. Require human approval for create_support_ticket in production

4. Deploy MCP over HTTP + host the Streamlit app behind SSO