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KARANNARESHMAKIM

RGM MCP Server

by KARANNARESHMAKIM
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Python
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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

Related MCP server: pricepilot-mcp

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

  1. Check the input schema for your NIQ files:

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

  2. 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"

  3. Compute price elasticities by market:

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

  4. Score past promos:

    "Score promo effectiveness by market from abt.csv"

  5. Get pricing recommendations (uses elasticity output):

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

  6. Build the promo calendar:

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

  7. 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

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license - not found
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maintenance

How are these scores calculated?

Maintenance

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–Release cycle
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