SAP Business One Order-to-Cash MCP Server

detect_seasonality

Identifies seasonal patterns in monthly revenue, quantity, or gross profit, optionally by customer/item. Outputs peak months, cycle length, and forecast model recommendation for demand planning.

Instructions

Analyse a historical monthly time series for seasonal patterns. Runs full additive decomposition (trend + seasonal + residual), computes ACF up to lag 24, seasonal strength score (Fs), peak/trough months, dominant cycle period, and recommends which forecast model to use. Works on revenue, quantity, or gross profit — optionally filtered by customer or item.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "detect_seasonality"
  }
}

Input Schema

Name	Required	Description
`measure`	No	revenue \| quantity \| gp (default: revenue)
`cardCode`	No	Filter by customer card code (optional)
`itemCode`	No	Filter by item code (optional)
`historyMonths`	No	Months of history to analyse (default 36, max 60)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 26, 2026

TDQS

A3.8/5.0

Behavior4/5

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

With no annotations provided, the description carries full responsibility for disclosing behavior. It does so unusually well, listing the actual algorithm steps: additive decomposition, ACF to lag 24, seasonal strength score, peak/trough months, dominant cycle period, and model recommendation. It does not discuss edge cases or minimum data requirements, but the core behavioral contract is transparent.

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?

The description is two sentences with no filler. It front-loads the primary purpose, then compactly lists the analytical outputs and the supported inputs. Every clause contributes either to what the tool does or what it can be applied to.

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?

Given the absence of an output schema, the description does a good job listing what the agent can expect: decomposition components, ACF, Fs, peak/trough months, dominant cycle, and a model recommendation. It is less explicit about minimum history requirements, result formatting, and how to interpret the recommendation, but it is sufficient for an agent to select and invoke the tool correctly in most situations.

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?

The input schema already provides full descriptions for all four parameters, including defaults and allowed values, so the baseline is 3. The description restates that it works on revenue, quantity, or gross profit and can be filtered by customer or item, but adds little semantic value beyond the schema. historyMonths is not elaborated in the description, though the schema documents it.

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

Purpose4/5

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

The description opens with a specific verb and resource: 'Analyse a historical monthly time series for seasonal patterns.' It clearly identifies the analytical task and lists concrete outputs, making the tool's purpose unmistakable. It does not explicitly name or contrast sibling tools like forecast_sales, but the content is specific enough to distinguish it from forecasting and sales aggregation tools.

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

Usage Guidelines3/5

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

There is no explicit 'when to use this vs. alternatives' guidance, but the phrase 'recommends which forecast model to use' implies this is a precursor to forecasting. The description also states valid measures and optional filters, giving practical context. However, it never says 'use this instead of forecast_sales' or 'use this before forecasting,' leaving the routing decision partly to inference.

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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