Modelforge MCP Server

ModelForge

Bulge-tier Excel financial model factory for credit & structured finance. Every cell live-formulated. Every number traceable back to the source document page it came from.

Built for Italian private capital (unitranche, minibond, project finance, RE, NPL, structured credit) — extensible to any asset class.

Use it inside Claude Code, Cursor, ChatGPT Enterprise (MCP-native)

PyPI name: modelforge-finance (the unscoped modelforge was taken by source{d}'s ML library). Import name stays modelforge.

pip install "modelforge-finance[mcp,export]"

# wire into your MCP client config:
{
  "mcpServers": {
    "modelforge": { "command": "modelforge-mcp" }
  }
}

Then in your AI assistant:

"Build me a unitranche LBO model from this YAML spec, export the committee deck."

Tools available: list_templates · build_model · qc_workbook · list_sources · lineage_walk · ingest_dataroom · export_pptx · export_docx.

See GTM_STRATEGY.md and SCORECARD_v2.md for the full GTM thesis and competitor comparison (vs Rogo / Hebbia / Macabacus / o11).

The architectural principle

LLMs produce specs + sources + narrative. Deterministic Python produces the workbook.

The LLM never writes a number into a cell. It writes a typed YAML spec with source IDs. A deterministic builder emits the Excel via openpyxl. A QC gate validates before export. Excel is a render of a linkage graph; the graph is persisted to SQLite and is the canonical artifact.

Quality standards (bulge-tier, non-negotiable)

Formatting

• Blue = hardcoded input. Black = formula. Green = cross-sheet link. Red = warning.
• No mixed formulas (no magic numbers embedded). Named ranges for every driver.
• Costs NEGATIVE (sign convention enforced and checked).
• EN primary labels, IT secondary.
• Historical vs Projected column separator, obvious.
• Check row at top of every sheet (BS balance, CFS tie, covenant headroom — TRUE or 0).

Sourcing

• Every hardcoded cell has a comment with source ID (S-001, S-002, ...).
• Sources sheet lists each source: doc, page, publisher, date, URL, verified-flag.
• Assumptions (not sourced) tagged A-001 with rationale + confidence H/M/L.

Scenarios

• WORST / BASE / BEST toggle on Assumptions. Drives every sheet via CHOOSE.
• Every sheet respects the toggle — no orphan assumptions.

Audit

• QC sheet with 12 automated checks, all must pass.
• Revision log on Cover.
• Named ranges mandatory.
• Print areas set. Print-ready on every sheet.

Quick start

cd "C:/Users/lukep/Desktop/Projects AI/ModelForge"
pip install -e .
modelforge build examples/unitranche_cdmo.yaml
modelforge qc output/unitranche_cdmo.xlsx

Data-room ingestion (v0.3.1)

Turn a directory of PDFs, XLSXs and CSVs into a validated ModelForge YAML spec using Claude Opus. Every extracted number traces back to a doc page via the auto-built Sources registry.

pip install -e .[ingest]                # installs anthropic, pdfplumber, pypdf
export ANTHROPIC_API_KEY=sk-ant-...      # required

modelforge ingest path/to/dataroom/ \
    --template project_finance \
    -o output/my_deal.yaml --verbose

# Review output/my_deal.yaml + output/my_deal.ingestion.md
# (INGESTION_REPORT.md lists every extracted field, S-id, confidence)

modelforge build output/my_deal.yaml     # produces the workbook
modelforge qc output/my_deal.xlsx        # 8/8 quality gate

Supported template: project_finance (MVP). Templates 1, 3, 5-8 queued for v0.3.2. See PRD_v03_dataroom_ingestion.md for the full spec.

Package layout

modelforge/
├── graph/            # First-class linkage graph (nodes, edges, SQLite persistence)
├── spec/             # Pydantic schemas per template
│   ├── base.py       # Source, Assumption, Scenario, Target (shared types)
│   └── unitranche.py # Template 1: Unitranche LBO
├── builder/          # Deterministic openpyxl writer
│   ├── styles.py     # Bulge-tier formatting library
│   ├── formulas.py   # Formula string builders
│   ├── i18n.py       # EN/IT label dictionary
│   ├── workbook.py   # Top-level builder
│   └── sheets/       # One module per sheet (cover, sources, assumptions, ...)
├── qc/               # Quality gate (12 checks + PDF report)
├── data/             # Market data loaders (Damodaran, ECB, Borsa minibond)
└── cli.py            # modelforge build|qc|sources|inspect

Templates (all shipped)

1. ✅ Unitranche LBO — Italian mid-market direct lending (Cash sweep + IFRS 9 EIR)
2. ✅ Minibond — Banca Finint territory (Gross YTM + Net YTM + Italian WHT)
3. ✅ Credit Memo — Extends Unitranche with recovery waterfall + PD×LGD×EAD
4. ✅ Project Finance — Construction + operating phases, DSCR-driven
5. ✅ Real Estate — NOI build, exit cap, LP/GP promote waterfall
6. ✅ NPL Portfolio — Collection curves, servicing fees, senior/mezz capital structure
7. ✅ Structured Credit — Tranche waterfall with attachment/detachment points
8. ✅ 3-Statement — P&L + BS + CFS with BS balance integrity check

Run modelforge list-templates to see them all. Each ships with an anonymized Italian example YAML in examples/.

The pitch

Bulge-tier Excel models, every cell live-formulated, every number traceable back to the data room page it came from.