PyMC Marketing MCP

LLMs can explain marketing data. They should not invent marketing science.

PyMC Marketing MCP gives MCP-compatible agents a controlled interface to Bayesian Marketing Mix Modeling with PyMC-Marketing. It handles dataset checks, MMM fitting, diagnostic gating, posterior contribution analysis, total and marginal iROAS, counterfactual spend scenarios, and constrained budget allocation while keeping the statistical computation inside PyMC-Marketing.

The core boundary is simple: the agent frames the business question and explains evidence. PyMC-Marketing computes the statistical quantities. The MCP layer validates inputs, persists artifacts, applies decision gates, reports uncertainty, and records provenance.

User question
  -> AI agent
  -> MCP tool
  -> dataset / model validation
  -> PyMC-Marketing
  -> posterior evidence
  -> decision gate
  -> structured result + uncertainty + provenance
  -> AI explanation

Version 0.3.0 Release

Version 0.3.0 delivers comprehensive Bayesian statistical verification, model lineage, lift test calibration, cross-validation, and multi-core accelerated testing:

• Time-Slice Cross-Validation: cross_validate_mmm evaluates out-of-sample predictive accuracy across temporal folds with PyMC-Marketing's TimeSliceCrossValidator.

• Prior Sensitivity Analysis: evaluate_prior_sensitivity quantifies channel rank shifts under alternative adstock and saturation priors.

• Lift Test Calibration: calibrate_mmm incorporates real or synthetic experimental incrementality lift tests directly into model likelihood with full lineage tracking.

• Extrapolation Risk Guard: Spend scenarios or optimization allocations exceeding 1.5x historical 95th percentile spend automatically trigger actionable warnings.

• Multi-Core Accelerated Testing: Pytest suite runs concurrently via pytest-xdist, completing 46 unit, integration, and full Bayesian sampling tests in ~50 seconds.

• Official MCP 2.0.0 Transports: Fully tested stdio and Streamable HTTP clients with dynamic port discovery and structured error envelopes.

See docs/DECISION-INTEGRITY.md and docs/VERIFICATION-MATRIX.md for details.

Related MCP server: simba-mcp

Current compatibility

• Python 3.12 to 3.13

• PyMC-Marketing >=1.0.0

• PyMC >=6.0.0

• ArviZ >=0.21,<2.0

• Official MCP Python SDK v2 (mcp>=2.0.0)

• NetCDF4 storage via h5netcdf and h5py

• CSV and Parquet datasets

• SQLite metadata and NetCDF model artifacts for local deployment

Install

uv sync --extra dev
uv run pytest -n auto -v

Run with stdio

uv run marketing-mcp --transport stdio

Run with Streamable HTTP

uv run marketing-mcp --transport streamable-http --host 127.0.0.1 --port 8000
# endpoint: http://127.0.0.1:8000/mcp

Docker

docker compose up --build

Core flow

1. register_dataset

2. inspect_dataset

3. validate_dataset

4. fit_mmm

5. diagnose_mmm

6. get_channel_contributions

7. get_incremental_roas

8. get_response_curves

9. simulate_budget

10. optimize_budget

11. cross_validate_mmm

12. evaluate_prior_sensitivity

13. calibrate_mmm

14. compare_models

15. archive_model

16. Explain the posterior result, diagnostics, assumptions, and provenance

Synthetic demo

uv run marketing-mcp-demo --fast

Repository map

src/marketing_mcp/
  mcp/            protocol tools + resources
  services/       application workflows (modeling, dataset, decision, diagnostics)
  domain/         validation, diagnostics engine & gate, allocation & extrapolation
  adapters/       PyMC-Marketing 1.0.0 boundary
  storage/        SQLite metadata + NetCDF artifacts
  schemas/        typed Pydantic contracts
tests/
  statistical/    real NUTS sampling, panel MMM, calibration, cross-validation
  integration/    MCP stdio/HTTP protocol, persistence lifecycle
  unit/           domain logic, failure datasets, security guardrails
docs/             architecture, contracts, safety, security, verification matrix