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:
0.0.32— Phase 3 (build + drive). 25 tools: build_tree / build_mcda / edit_tree (incl. add/remove options & outcomes) / set_input_distribution (put a Vose distribution on an input) / build_control_panel / export_tree_json / import_tree_json / import_precisiontree, read + roll + verify, run_scenarios (what-if comparison), plus run_evpi / run_evii / run_risk_profile / run_robustness / run_sensitivity / run_decision_report (strategy/policy/brief/mcda/force-to-outcome/two-way) / run_analysis / read_sheet over ModelChoice's headless commands.
Tools
| Tool | What it does |
|---|---|
build_tree |
Build a tree from a structured description and write it into Excel (dry-run by default). Validates + rolls it back so you confirm the recommendation before writing. The build-from-a-prompt path. |
build_mcda |
Build a multi-criteria (MCDA) model — for choices that aren't pure money. Give the tree + criteria (ordinal options, weights, direction), aggregation, and each terminal's option per criterion; the add-in computes the composite scores. Weights can be entered directly or derived from an AHP pairwise-comparison matrix (weight_source="ahp" → eigenvector weights + consistency ratio). Drives MC_ApplyMcda_Auto. |
edit_tree |
Tweak an existing tree — change probabilities, payoffs, labels, or the objective, add or remove whole options/outcomes (add_option / add_branch / remove_branch), then re-roll it (dry-run by default). The "tweak it by talking" path. |
set_input_distribution |
Make a tree input uncertain — put a ModelRisk Vose* distribution on a branch's cash flow or probability (like typing a distribution into the cell in the UI). The decision-tree half of a Monte Carlo: once inputs are distributions, run the simulation with modelrisk-mcp to get the tree's outcome distribution. Pure store-edit + re-render — no add-in command. |
build_control_panel |
Lift the tree's inputs into a control panel at the top of the sheet — every probability and cash flow as a labelled cell, with the tree linked back to it, so you drive the whole model from one input block. Drives MC_BuildControlPanel_Auto. |
export_tree_json / import_tree_json |
Round-trip a tree's raw ModelChoice JSON — export to save/share/version, import (validated) to write it back into a workbook. |
import_precisiontree |
Import a PrecisionTree workbook (.xls/.xlsx) into ModelChoice — converts a copy (original untouched). Drives MC_ImportPrecisionTree_Auto. |
open_workbook |
Open a workbook (.xlsx) from disk in the running Excel so the other tools can act on it. Reports its sheets + any ModelChoice tree sheets; reuses an already-open workbook of the same name. |
close_workbook |
Close an open workbook by file name (counterpart to open_workbook). Discards unsaved changes by default; pass save=True to save first. |
license_status |
Report the ModelChoice licence state (licensed / trial / expired / not activated). Building and analysis actions require a full licence; reading trees works regardless. Read-only. |
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_scenarios |
What-if comparison — give named scenarios (bundles of input changes); each is rolled back and compared to the baseline (EV, optimal decision, Δ, whether the decision flips, which scenario wins). Pure Python, no Excel needed. |
run_evpi |
Expected Value of Perfect Information for the active tree — the most you'd pay for perfect information before deciding. Drives ModelChoice's headless MC_EVPI_Auto. |
run_risk_profile |
The outcome distribution for each decision option — expected value, min, max, std dev, plus the cumulative-probability table. Shows downside/upside, not just the average. Drives MC_RiskProfile_Auto. |
run_utility |
Apply a risk attitude (utility function) — returns the certainty equivalent, risk premium, and the optimal decision under risk aversion (can differ from the EV choice). Drives MC_Utility_Auto. |
run_evii |
Expected Value of Imperfect Information for a specific test — pass the target chance node and a likelihood matrix P(signal|state); returns EVII, its net value after cost (worth running?), and the EVPI ceiling. Drives MC_EVII_Auto. |
run_decision_report |
Run a report — strategy_table, policy_suggestion, decision_brief, mcda_report, or force_to_outcome — and read it back (rows + label→value pairs + sheets) in one call. |
run_analysis |
Run any decision-analysis (robustness, sensitivity, strategy_table, policy_suggestion, decision_brief, mcda_report, risk_profile, evpi) and report the result sheets it produced. |
run_robustness |
Run the robustness ("break the decision") analysis and return a structured read — verdict, score, and the minimum input change that flips the decision. |
run_sensitivity |
Run one-way sensitivity and return the tornado-ordered report (which assumptions the decision is most sensitive to) + baseline EV + sheets. |
run_decision_report |
Run a decision report — strategy_table, policy_suggestion, decision_brief, or mcda_report — and read the primary result sheet back in one call (rows + label→value pairs + related sheets). |
read_sheet |
Read a result sheet's cells back (numbers / text), e.g. the robustness verdict or a sensitivity report. |
Prompts: /design-decision-tree walks an analyst through building a tree from a description (or pasted data) and analysing it. /decision-tree-monte-carlo then turns it into a Monte Carlo — put Vose* distributions on the uncertain inputs and run the simulation in modelrisk-mcp to get the distribution of the tree's expected value.
Resources: modelchoice://guide/* — curated decision-analysis guidance (building trees, EVPI vs EVII, which analysis answers which question, common pitfalls) the model can read for grounding.
Install
Recommended (works on every current Claude version): pip install modelchoice-mcp, then add the server to Claude Desktop's claude_desktop_config.json (a modelchoice-mcp command entry, or uvx modelchoice-mcp) and restart Claude Desktop. Run standalone with uv run python -m modelchoice_mcp (stdio). (Excel + the ModelChoice add-in are still required for rendering trees.)
One-click .mcpb (Claude Desktop Extension): download modelchoice-mcp.mcpb from the latest release, open it (Claude Desktop → Settings → Extensions → Install Extension…), then restart Claude. The bundle ships the server as a standalone exe — no Python, no config edit.
⚠️ Known issue (Claude Desktop, latest Windows MSIX builds, e.g. 1.12603.x): the Extensions installer can silently do nothing when you pick a
.mcpb— no error, no install. This is a Claude Desktop installer bug (it fails before logging), not a problem with the bundle (it validates withmcpband installs fine once the client is fixed). Until Anthropic patches it, use thepip install+ config path above.
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_Storepayload (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 — read + rollback engine (done): parse
_MC_Store, reconstruct each tree, roll back to EV + optimal policy. Validated against ModelChoice's own C# rollback test. - Phase 1 — the MCP server (done):
list_trees,get_tree,roll_up, proven live against a real workbook.verify_rollbackcross-checks our EVs against theMC_V_<id>named ranges (live-verified: 4/4 nodes, 0 diff). CI (ruff + mypy + pytest) + a tag-driven PyPI release pipeline. - Phase 2 — drive analyses (done):
run_analysis+read_sheetdrive any headlessMC_*_Autocommand; structured readers forrun_robustness,run_sensitivity,run_risk_profile, andrun_decision_report(strategy / policy / brief / mcda / force-to-outcome / two-way), plus standalonerun_evpi(AB#2558) andrun_evii(AB#2568). - Phase 3 — build / edit / extend trees (done):
build_tree,edit_tree(incl. add/remove options & outcomes),build_control_panel,export_tree_json/import_tree_json,import_precisiontree(AB#2632),run_utility(risk attitude, AB#2635),build_mcda(multi-criteria, AB#2637), andset_input_distribution(put aVose*distribution on an input — the decision-tree half of a Monte Carlo). All live-verified against the shipped add-in.
The original phased roadmap is delivered. Candidate next steps (not yet scheduled):
- Simulation hand-off (cross-server): the
decision-tree-monte-carloprompt guides assigning distributions → wrapping the root EV as a ModelRisk output → running the simulation inmodelrisk-mcp→ reading the EV's output distribution back. A thin orchestration tool could automate the hand-off end-to-end. AHP weight elicitation for MCDA— done (0.0.25, AB#2646):build_mcdaaccepts a pairwise-comparison matrix (weight_source="ahp") and derives the weights + consistency ratio via the add-in'sAhpCalculator.
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.
Metadata
Release files for modelchoice-mcp 0.0.32
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
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| modelchoice_mcp-0.0.32-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 198.9 kB
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