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mcostigliola321

shopify-forecast-mcp

by mcostigliola321
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
SHOPIFY_FORECAST_SHOPYesYour store domain (e.g. mystore.myshopify.com)
SHOPIFY_FORECAST_ACCESS_TOKENYesAdmin API access token (from custom app)

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
forecast_revenueA

Forecast total store revenue over a future horizon using TimesFM 2.5.

Returns a markdown summary and table with point forecast and 80% confidence band (q10-q90).

forecast_demandB

Forecast demand by product, collection, or SKU using TimesFM 2.5.

Returns a ranked markdown table showing projected demand per group
with confidence bands. When group_value is 'all', forecasts the top N
groups by historical volume.
analyze_promotionB

Analyze a past promotion's impact vs baseline: revenue lift, order lift, AOV change, post-promo hangover, and product cannibalization.

compare_periodsC

Compare two time periods across revenue, orders, units, AOV, discount rate, and units per order.

get_seasonalityB

Identify seasonal patterns in store data by day of week, month, or quarter.

detect_anomaliesB

Detect anomalous days where actual values fell outside expected forecast bands.

compare_scenariosA

Compare 2-4 promotional scenarios with what-if forecasting.

Each scenario specifies a promo period and discount depth. Returns a side-by-side markdown table with revenue projections, confidence bands, and a recommendation for the best-performing scenario.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.6/5.0

Scored across 7 tools

Disambiguation5/5

Each tool has a distinct purpose: analyze promotion impact, compare periods, compare scenarios, detect anomalies, forecast demand, forecast revenue, and get seasonality. No overlap in functionality.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern in snake_case (e.g., analyze_promotion, compare_periods, forecast_demand). No mixing of naming conventions.

Tool Count5/5

Seven tools is appropriate for a forecasting and promotion analysis MCP server. It covers core needs without being too few or too many.

Completeness4/5

The tool set covers key forecasting, trend comparison, anomaly detection, and seasonality analysis. Minor gap: no tool for managing underlying data or listing available products/collections, but the surface is comprehensive for analysis.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources