# Google Meridian Mcp

> Cloud-agnostic Model Context Protocol (MCP) server for Google Meridian & Marketing Mix Modeling (MMM). Grounded in first principles, data science control points, Bayesian prior calibration, and workflow diagnostics.

- **Type:** MCP server
- **Install:** `agentstack add mcp-muhammad1azmi-google-meridian-mcp`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [muhammad1azmi](https://agentstack.voostack.com/s/muhammad1azmi)
- **Installs:** 0
- **Category:** [Integrations](https://agentstack.voostack.com/c/integrations)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [muhammad1azmi](https://github.com/muhammad1azmi)
- **Source:** https://github.com/muhammad1azmi/google-meridian-mcp

## Install

```sh
agentstack add mcp-muhammad1azmi-google-meridian-mcp
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Google Meridian MCP Server (`google-meridian-mcp`)

[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://nodejs.org/)
[](https://modelcontextprotocol.io)

A **Cloud-Agnostic, First-Principles Model Context Protocol (MCP) server** for **Google Meridian** and Marketing Mix Modeling (MMM). 

It empowers data scientists and AI assistants (Cursor, Claude Desktop, Antigravity) to create, audit, calibrate, and optimize mathematically sound, causally valid Marketing Mix Models with zero cloud vendor lock-in.

---

## 💡 Why First Principles Over Rigid Scripts?

Most assistant implementations rely on hardcoded procedural scripts (`SKILL.md`). In real-world data science, rigid scripts break because **every business, industry, and marketing dataset is unique**:
- A retail brand with 50 DMAs operates differently than a B2B SaaS startup with national data.
- App install targets (`NON_REVENUE`) require different priors than revenue models (`REVENUE`).
- Specific channel CPMs, flighting patterns, and Lift Tests vary across campaigns.

**First Principles never change.** Causal identification (DAGs), carryover/saturation physics (Adstock & Hill curves), Bayesian probability calibration, NUTS MCMC geometry ($\hat{R} < 1.05$), and KKT convex budget optimization apply universally to every dataset on Earth.

By anchoring this MCP server in **First Principles & Dynamic Data Science Tools**, the co-pilot adapts seamlessly to any specific edge case while guaranteeing mathematical rigor.

---

## 🏛️ The 4 Mandate Pillars

```
┌────────────────────────────────────────────────────────────────────────────────────────┐
│ 1. EVERY CONTROL POINT   │ Covers Data Inputs, Controls/DAG, Baseline, Adstock,        │
│                          │ Hill Saturation, Bayesian Priors, MCMC, Optimization.       │
├──────────────────────────┼─────────────────────────────────────────────────────────────┤
│ 2. END-TO-END WORKFLOW   │ Guides the 5 phases & 3 iteration loops (Convergence        │
│                          │ Diagnostics ➔ Causal Plausibility ➔ Lift Calibration).      │
├──────────────────────────┼─────────────────────────────────────────────────────────────┤
│ 3. FIRST PRINCIPLES      │ Grounded in causal inference, probability theory, HMC/NUTS  │
│                          │ sampling geometry, and convex optimization math.            │
├──────────────────────────┼─────────────────────────────────────────────────────────────┤
│ 4. PLATFORM AGNOSTIC     │ 100% portable with zero cloud vendor lock-in. Runs locally, │
│                          │ in Docker, or on AWS, Azure, GCP, Railway, Render, etc.     │
└────────────────────────────────────────────────────────────────────────────────────────┘
```

---

## 🛠️ Complete MCP Tool Suite (11 Tools)

| Category | Tool | Parameters | Description |
| :--- | :--- | :--- | :--- |
| **Control Points** | `get_control_point_guide` | `control_point: str` | Operational parameters, math formulas, and bounds for all control points. |
| **Workflow** | `get_mmm_workflow_guide` | `phase: str` | Decision trees and iteration rules for the 5 modeling phases & 3 iteration loops. |
| **Prior Math Engine** | `calculate_bayesian_prior` | `point_estimate, ci_lower, ci_upper` | Converts 95% CIs from Lift Tests into exact Meridian LogNormal ($\mu, \sigma$) prior parameters. |
| **Spec Auditor** | `audit_model_first_principles` | `config_json: str` | Audits model specs for identifiability, knot density, prior variance, and Hill parameter bounds. |
| **EDA Engine** | `run_eda_checks` | `config_json: str` | Pre-modeling EDA checks (`VIFSpec`, `PairwiseCorrSpec`, `DataParameterRatioArtifact`). |
| **Model Reviewer** | `run_model_review_checks` | `check_type: str` | Diagnostic checks (`BayesianPPPCheck`, `PriorPosteriorShiftCheck`, `ImplausibleROICheck`). |
| **Code Synthesizer** | `synthesize_meridian_code` | `pipeline_stage: str` | Generates clean, portable, cloud-agnostic Python code for Google Meridian pipelines. |
| **Data Utility** | `generate_schema_template` | `n_weeks, n_geos, n_channels` | Generates synthetic CSV schema templates matching Meridian's input format. |
| **Documentation** | `list_doc_sources` | `category: str` | Lists documentation sources filtered by category. |
| **Documentation** | `fetch_docs` | `url: str` | Fetches and parses documentation pages/GitHub code to Markdown. |
| **Documentation** | `search_doc_topics` | `query: str` | Searches Meridian topic index (Adstock, Hill curves, NUTS, Priors, etc.). |

---

## ⚡ Quick Connect (Remote SSE Mode)

Add this to your IDE's `mcp_config.json`:

```json
{
  "mcpServers": {
    "google-meridian": {
      "url": "https://google-meridian.mcp.borobudur.ai/sse"
    }
  }
}
```

---

## 💻 Local Setup & Running

### Option A: Python FastMCP Setup (Recommended)
```bash
# Install dependencies
pip install -r requirements.txt

# Run server in stdio mode
python server.py
```

`mcp_config.json`:
```json
{
  "mcpServers": {
    "google-meridian-mcp": {
      "command": "python",
      "args": [
        "C:/path/to/google-meridian-mcp/server.py"
      ]
    }
  }
}
```

### Option B: Node.js Setup
```bash
npm install
npm start
```

---

## 📄 License

[MIT License](LICENSE). Open source and free for the community.

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [muhammad1azmi](https://github.com/muhammad1azmi)
- **Source:** [muhammad1azmi/google-meridian-mcp](https://github.com/muhammad1azmi/google-meridian-mcp)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/mcp-muhammad1azmi-google-meridian-mcp
- Seller: https://agentstack.voostack.com/s/muhammad1azmi
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.