Bulge-tier financial model factory — live formulas, source-traced cells, 14 templates.
Bulge-tier financial model factory — live formulas, source-traced cells, 14 templates.
Valid MCP server (1 strong, 2 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.
4 files analyzed · 1 issue found
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Set these up before or after installing:
Environment variable: ANTHROPIC_API_KEY
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-whatsonyourmind-modelforge": {
"env": {
"ANTHROPIC_API_KEY": "your-anthropic-api-key-here"
},
"args": [
"modelforge-finance"
],
"command": "uvx"
}
}
}From the project's GitHub README.
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.
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).
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.
Formatting
Sourcing
Sources sheet lists each source: doc, page, publisher, date, URL, verified-flag.Scenarios
Audit
QC sheet with 12 automated checks, all must pass.cd "C:/Users/lukep/Desktop/Projects AI/ModelForge"
pip install -e .
modelforge build examples/unitranche_cdmo.yaml
modelforge qc output/unitranche_cdmo.xlsx
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.
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
Run modelforge list-templates to see them all. Each ships with an anonymized Italian example YAML in examples/.
Bulge-tier Excel models, every cell live-formulated, every number traceable back to the data room page it came from.
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