modelchoice-mcp 0.0.1

An open Model Context Protocol server for Vose Software's ModelChoice decision-tree add-in for Excel.

ModelChoice MCP

An open Model Context Protocol server for Vose Software's ModelChoice — the decision-tree add-in for Excel.

A sibling to modelrisk-mcp: where that server brings Monte Carlo risk modelling into a conversation, this one brings decision analysis — building, reading, rolling back, and analysing decision trees in Excel.

Status: 0.0.1 — Phase 1 MCP server (read-only). Three tools (list_trees, get_tree, roll_up) over a proven read path. Building/analysis tools are on the roadmap below.

Tools

Tool	What it does
list_trees	List the decision trees in a workbook with node-type counts.
get_tree	Full structure of one tree — decision / chance / terminal nodes, branches, probabilities, values.
roll_up	Roll the tree back to its expected values and optimal policy — the decision recommendation, in plain English.
verify_rollback	Cross-check our rollback against the MC_V_<id> cells ModelChoice itself wrote — a correctness guarantee when the tree has been rendered.

Run it: uv run python -m modelchoice_mcp (stdio), or wire modelchoice-mcp into Claude Desktop like any MCP server.

How it works

ModelChoice is a C# .NET (Excel-DNA) add-in. Unlike ModelRisk it exposes no COM object model and no compute worksheet functions — but it persists the entire decision tree as JSON in a very-hidden worksheet (_MC_Store), and the rollback logic is fully captured in that model. So this server can:

• Read every tree in a workbook by reassembling the _MC_Store payload (chunked across columns, v2-envelope or v1-legacy format) and parsing the model JSON.
• Roll back each tree to its expected values and optimal policy in pure Python — reproducing ModelChoice's own rollback (terminal payoff = accumulated branch values; chance = auto-normalized probability-weighted EV; decision = max/min by Maximize). No add-in needs to be loaded — it reads the saved model directly.

The Python roller is validated against ModelChoice's own authoritative C# test (Model_Validate_Compile_Rollback_Works): a decision/chance tree that rolls back to EV 50 with the optimal option selected — and matches exactly.

Roadmap

• Phase 0 (done): the read + rollback engine — parse _MC_Store, reconstruct each tree, roll back to EV + optimal policy. Validated against ModelChoice's own C# rollback test.
• Phase 1 (done): the MCP server — list_trees, get_tree, roll_up, proven live against a real workbook.
• Phase 1b (done): verify_rollback cross-checks our EVs against the MC_V_<id> named ranges; CI (ruff + mypy + pytest) and a tag-driven PyPI release pipeline; wheel + sdist build. (First publish awaits a PyPI trusted-publisher; the live MC_V_ cross-check awaits a real rendered tree.)
• Phase 2: drive analyses. ModelChoice's analyses (sensitivity, EVPI, EVII, strategy table, robustness) are currently UI/modal-dialog only; the clean path — since we own the source — is to add headless [ExcelCommand] entry points mirroring the existing MC_RiskProfile_Auto, then invoke + read them via Application.Run.
• Phase 3: build/edit trees — write the model JSON into _MC_Store and trigger a re-render.

Architecture

A separate server that reuses the patterns proven in modelrisk-mcp (xlwings bridge, dry-run-by-default safety on any future writes, packaging, MCP-registry flow). Decision analysis and Monte Carlo are distinct domains, so they ship as distinct servers.

Development

uv sync --extra dev
uv run pytest        # parser/roller + store-format tests (no Excel needed)

MIT licensed.