ReverseFCFFMCP

A specialized Model Context Protocol (MCP) server for running Reverse DCF / FCFF Valuations.

Instead of asking "What is the value given my assumptions?", this engine asks: "Given a target value (e.g., $2T), what combination of growth, margins, reinvestment, and market share must be true?"

Features

• Three Reinvestment Methods: Sales-to-Capital, ROIC-based, and Explicit.

• Discounting Conventions: Mid-year (Damodaran default) and End-year.

• Terminal Value Consistency: Enforces terminal reinvestment rate = g / ROIC.

• Reverse Solvers: Root-finding (brentq) to solve for Margin, Growth, Sales-to-Capital, or ROIC to hit a target Enterprise Value.

• AI Market Layer: Translate terminal revenue into required AI market share or required industry size.

• Governance Layer: Input sanitization, math guardrails (e.g., preventing WACC ≤ Terminal Growth singularities), and automated evals.

Related MCP server: modelforge

Available MCP Tools

This server exposes the following tools to the MCP client:

1. forward_dcf

Runs a standard forward Discounted Cash Flow valuation.

• Inputs: current_revenue, revenue_cagr, ebit_margin, wacc, terminal_growth, forecast_years, net_cash, reinvestment_method, sales_to_capital / roic, etc.

• Output: JSON containing projected Revenues, EBITs, NOPATs, Reinvestments, FCFFs, Discount Factors, Present Values, Terminal Value, Enterprise Value, and Equity Value.

2. reverse_solve_margin

Solves for the exact EBIT margin required to achieve a target Enterprise Value, given fixed assumptions for growth and reinvestment.

• Inputs: target_ev, fixed (a dictionary of the remaining DCF assumptions).

• Output: ebit_margin (float) and converged (boolean).

3. reverse_solve_growth

Solves for the exact Revenue CAGR required to achieve a target Enterprise Value, given fixed assumptions for margin and reinvestment.

• Inputs: target_ev, fixed (a dictionary of the remaining DCF assumptions).

• Output: revenue_cagr (float) and converged (boolean).

4. validate_damodaran_consistency

Runs structural validation checks on a set of DCF inputs to catch common modeling errors.

• Inputs: A full DCFInput object.

• Output: A list of warning objects detailing any violated rules (e.g., wacc_le_terminal_growth, terminal_growth_ceiling, fundamental_growth_ceiling).

5. feasibility_grid

Performs a grid search across combinations of growth, margin, and sales-to-capital to determine which combinations successfully hit a target EV within a specified tolerance.

• Inputs: target_ev, fixed (base assumptions), param_grid (dictionary of arrays to test), tolerance.

• Output: A list of dictionaries representing each scenario, its resulting EV, and a feasible boolean flag.

6. run_evals

Runs automated governance evaluations to verify the engine's mathematical integrity.

• Inputs: None.

• Output: JSON verifying if the reverse-solver round-trips accurately and if the hard guardrails successfully block invalid inputs.

Claude Desktop Integration (Local stdio)

To use this MCP server locally with Claude Desktop, you must have the uv package manager installed (pip install uv).

1. Open Claude Desktop and navigate to Settings > Developer > Edit Config.

2. This will open your claude_desktop_config.json file.

3. Add the reverse-fcff-mcp configuration to your mcpServers object. It should look like this:

{
  "mcpServers": {
    "reverse-fcff-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/YOUR_USERNAME/reverse-fcff-mcp.git",
        "python",
        "-m",
        "reverse_fcff_mcp.server"
      ]
    }
  }
}