pymc-marketing-mcp
Provides a controlled interface for Bayesian Marketing Mix Modeling, letting AI agents validate datasets, fit and diagnose MMMs, analyze channel contributions and ROI, simulate and optimize budgets, calibrate with lift tests, and cross-validate models—all with statistical verification, uncertainty r
- Score
- 22.5571 signal
- Evidence
- 1 star
- Last commit
- as last read from GitHub; most reads are from 2 Sep 2026 or later
- Listed
Install
No one-command install. Set it up from its source.
What it is
Provides a controlled interface for Bayesian Marketing Mix Modeling, letting AI agents validate datasets, fit and diagnose MMMs, analyze channel contributions and ROI, simulate and optimize budgets, calibrate with lift tests, and cross-validate models—all with statistical verification, uncertainty r
When to use it
Provides a controlled interface for Bayesian Marketing Mix Modeling, letting AI agents validate datasets, fit and diagnose MMMs, analyze channel contributions and ROI, simulate and optimize budgets, calibrate with lift tests, and cross-validate models—all with statistical verification, uncertainty r
How to install / invoke
See Glama for the install config.
Notes
Listed from the Glama MCP registry.