Schema | forecast-mcp

forecast-mcp

Describes the environment variables required to run the server.

Name	Required	Description	Default
No arguments

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.

Features and capabilities supported by this server

Protocol revision2025-11-25

Capability	Details
`tools`	{ "listChanged": true }
`logging`	{}
`prompts`	{ "listChanged": false }
`resources`	{ "subscribe": false, "listChanged": false }
`extensions`	{ "io.modelcontextprotocol/ui": {} }
`experimental`	{}

Functions exposed to the LLM to take actions

Name	Description
list_skusA	List every SKU/series id available in the loaded dataset.
classify_demand_patternB	Classify a SKU's demand pattern (Syntetos-Boylan: smooth / erratic / intermittent / lumpy / cold_start) and the model tier it routes to.
forecast_seriesA	Forecast future demand for a SKU. Automatically classifies the series first and routes to the matching model: mean fallback for cold-start SKUs, TSB for intermittent demand, AutoETS for regular continuous demand.
evaluate_forecastA	Backtest a SKU's forecast: hold out the last `test_size` days, forecast them from the remaining history, and score with MASE (scale-free, so it's comparable across SKUs with very different demand volumes).
recommend_replenishmentA	Recommend a reorder quantity for a SKU: forecasts demand, then applies a reorder-point / safety-stock formula against current on-hand stock.
explain_forecastA	Return a plain-language explanation of why a SKU was routed to the model tier it was, for surfacing to a non-technical user.

Interactive templates invoked by user choice

Name	Description
No prompts

Contextual data attached and managed by the client

Name	Description
No resources

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/RohanSingh02/forecast-mcp'

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