RGM MCP Server

A Python MCP server exposing analytical tools for Revenue Growth Management (RGM) in the Consumer Goods and Services space. Designed to work with flat-file exports from NIQ / NielsenIQ (CSV or Parquet).

Tools

Tool	Description
get_nielsen_input_schema	Show the required and optional column schema for each NIQ input file before you start
build_analytical_base_table	Join NIQ sales + distribution + pricing exports into a clean, model-ready Analytical Base Table (ABT) with log-transformed columns
calculate_price_elasticity	OLS log-log regression → own-price & cross-price elasticity per segment (market, channel, SKU, etc.)
score_promo_effectiveness	Rolling-baseline lift %, incremental volume/revenue, and trade ROI per promo event
recommend_dynamic_pricing	Grid-search over ±N% price moves to find the revenue-maximising price given a margin floor and elasticity
optimize_promo_calendar	Greedy ROI-ranked promo event scheduling within total budget + max-events-per-SKU constraints
compute_competitive_price_index	Volume-weighted Competitive Price Index (own price / competitor price × 100) by category / brand / pack size / market

Requirements

• Python 3.10+
• Dependencies: fastmcp, pandas, pyarrow, numpy, scipy

Setup

# 1. Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate        # macOS/Linux
.\.venv\Scripts\activate         # Windows

# 2. Install dependencies
pip install -r requirements.txt

Running locally (stdio — for use with Bob / Claude Desktop)

python src/server.py

Register in your MCP client config (mcp.json):

{
  "mcpServers": {
    "rgm-mcp-server": {
      "command": "/absolute/path/to/.venv/bin/python",
      "args": ["/absolute/path/to/src/server.py"]
    }
  }
}

Typical workflow

0. Check the input schema for your NIQ files:

   "What columns do I need in my NIQ sales file?"

1. Build the ABT from your NIQ exports:

   "Build me the analytical base table from sales.csv, dist.csv, and pricing.csv, save to abt.csv"

2. Compute price elasticities by market:

   "What are the price elasticities by market from abt.csv?"

3. Score past promos:

   "Score promo effectiveness by market from abt.csv"

4. Get pricing recommendations (uses elasticity output):

   "Recommend prices for Chicago and New York given a 30% margin floor and COGS of $5"

5. Build the promo calendar:

   "Optimise a promo calendar for H2 2025 with a $200k budget"

6. Competitive price indexing:

   "Compute the competitive price index for BrandA across all markets"

NIQ input file schemas

Call get_nielsen_input_schema() at any time to get the full column spec. Quick reference:

sales file (sales_file)

Column	Type	Required	Description
period_end_date	date	✅	Week- or month-ending date (YYYY-MM-DD)
upc	string	✅	SKU / Universal Product Code
market	string	✅	NIQ retail geography
channel	string	✅	Trade channel (Grocery, Liquor, Club, etc.)
unit_sales	numeric	✅	Units sold in the period
dollar_sales	numeric	✅	Dollar revenue in the period
avg_price_per_unit	numeric	✅	Average shelf price (USD, no $ symbol)
brand	string	—	Brand name (required for CPI tool)
category	string	—	Category / sub-category
pack_size	string	—	Pack size / volume format
any_promo_flag	0 / 1	—	1 = promoted week (required for promo tools)
trade_spend	numeric	—	Trade spend in USD (required for promo ROI)

distribution file (distribution_file)

Column	Type	Required	Description
period_end_date	date	✅	Must match sales file exactly
upc	string	✅	Must match sales file exactly
market	string	✅	Must match sales file exactly
channel	string	✅	Must match sales file exactly
total_distribution_points	numeric	✅	NIQ TDP (% ACV weighted distribution)

pricing file (pricing_file)

Column	Type	Required	Description
period_end_date	date	✅	Must match sales file exactly
upc	string	✅	Must match sales file exactly
market	string	✅	Must match sales file exactly
channel	string	✅	Must match sales file exactly
competitor_brand	string	✅	Competitor brand name
comp_avg_price	numeric	✅	Competitor average shelf price (USD)
avg_price_per_unit	numeric	—	Own price (can be omitted if in sales file)

All three files are joined on period_end_date + upc + market + channel. Values must match exactly (case-sensitive) across files.

All column names can be overridden via tool parameters when calling each tool.

License

MIT