Automation Audit MCP

An MCP server that helps AI assistants evaluate business processes for automation. It collects structured discovery answers, estimates manual labor cost, scores automation potential, and stores completed audits in SQLite.

What it does

The server exposes tools that let an MCP client:

• create businesses and the processes they want to evaluate;
• calculate monthly hours and labor cost for repetitive work;
• score automation potential using frequency, rule clarity, data readiness, process stability, and required human judgment;
• save discovery answers and completed audit recommendations;
• list saved audits and retrieve a complete audit with its discovery answers.

It also includes a start_automation_audit prompt that guides the assistant through the assessment without inventing missing information or saving results before the user confirms them.

Available tools

Tool	Purpose
calculate_time_cost	Estimate monthly hours and labor cost.
calculate_automation_score	Produce a 0–100 automation score and category.
create_business	Store a business being audited.
create_process	Store a process associated with a business.
save_discovery_answer	Save or update one discovery response.
save_audit	Store a validated completed audit.
list_audits	List saved audits, newest first.
get_audit	Retrieve an audit and all related discovery answers.

Requirements

• Python 3.12 or newer
• uv
• An MCP-compatible client

Run locally

git clone https://github.com/zuperRuslana/automation-audit-mcp.git
cd automation-audit-mcp
uv sync
uv run python main.py

The server communicates over standard input/output, so it is normally started by an MCP client rather than used directly in a terminal.

MCP client configuration

Add a server entry like this to your MCP client configuration, replacing the directory with the absolute path where you cloned the project:

{
  "mcpServers": {
    "automation-audit": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/automation-audit-mcp",
        "run",
        "python",
        "main.py"
      ]
    }
  }
}

Example workflow

1. Create a business with create_business.
2. Create a process using the returned business ID.
3. Record discovery responses with save_discovery_answer.
4. Calculate time and labor cost with calculate_time_cost.
5. Calculate suitability with calculate_automation_score.
6. Review the recommendation and confirm it with the user.
7. Save the result with save_audit.
8. Retrieve the complete record with get_audit.

Data and validation

Audit data is stored locally in automation_audits.db. The database file is ignored by Git, so business data is not uploaded with the source code.

The server rejects negative cost inputs, ratings outside the supported range, orphaned database records, and audit categories that conflict with their score.

Tests

uv run pytest

The regression suite covers startup, input validation, database connection safety, foreign-key enforcement, and audit consistency.

Project structure

automation-audit-mcp/
├── main.py              # MCP server entry point
├── server.py            # Tools, prompt, validation, and SQLite storage
├── tests/               # Regression tests
├── pyproject.toml       # Python package and dependency metadata
└── uv.lock              # Reproducible dependency lock file