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SAP Business One Order-to-Cash MCP Server

SAP Business One Order-to-Cash MCP Server

by parmarnaresh86
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

forecast_item_demand

Predict future demand for an item using nine models, auto-selecting the best via backtesting. Get monthly forecasts, accuracy metrics, and a procurement suggestion including 20% safety stock.

Instructions

Predict future demand quantity for a specific item using all 9 models (SMA, WMA, EWMA, LinearTrend, Drift, Holt, Holt-Winters, SeasonalNaive, Median). Auto-selects best model via backtesting. Returns monthly forecasts per model, model accuracy (MAPE/MAE/RMSE), and a procurement suggestion (forecast qty + 20% safety stock).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "itemCode": "<itemCode>"
    },
    "name": "forecast_item_demand"
  }
}

Input Schema

NameRequiredDescriptionDefault
itemCodeYesSAP B1 item code
historyMonthsNoMonths of history to train on (default 12, max 36)
horizonMonthsNoNumber of months to forecast ahead (default 3, max 12)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of disclosing behavior. It does so by enumerating the 9 models, stating backtest-based auto-selection, and naming the exact outputs (monthly forecasts, MAPE/MAE/RMSE, procurement suggestion with 20% safety stock). It does not explicitly state that no records are written, but the 'Predict' and 'Returns' wording strongly imply a read-only analytic operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single dense sentence front-loads the main purpose and then efficiently lists models, selection method, and return values without any filler. Every phrase earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is strong for a tool with no output schema, explaining what the caller will receive: per-model forecasts, accuracy metrics, and a procurement suggestion. The input schema covers parameter meaning, so nothing critical is missing for selecting and invoking the tool. Minor gaps like edge cases or failure behavior exist, but they are not essential.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds context around the forecast outputs and model selection, but it doesn't provide additional semantic detail about historyMonths or horizonMonths beyond what the schema already gives.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the exact action ('Predict future demand quantity') and the resource ('a specific item'), then lists all 9 model names and the expected outputs. This is specific enough to distinguish it from sibling tools like forecast_sales or forecast_cash_flow.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description makes the use case clear: single-item demand forecasting with a procurement suggestion. It doesn't explicitly say 'use this instead of forecast_sales', but the item-level demand framing and safety-stock output imply the right context without ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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