SAP Business One Order-to-Cash MCP Server

detect_margin_erosion

Flag items whose gross profit percentage drops beyond a threshold over recent months, using trend analysis to highlight products at risk of unprofitability.

Instructions

Detect items whose gross profit percentage has declined by more than a threshold over recent months. Uses linear regression on per-item monthly GP% to identify a downward trend — flags items at risk of becoming unprofitable.

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_margin_erosion"
  }
}

Input Schema

Name	Required	Description	Default
`months`	No	Analysis window in months (default 12, max 24)
`erosionThresholdPct`	No	Minimum GP% drop to flag as eroding (default 5)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 26, 2026

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 transparency burden. It discloses the analytical approach (linear regression on monthly GP%), the threshold-based flagging, and the output concept of 'at risk' items. It does not explicitly state whether the tool is read-only, but the detect/flag language strongly implies a non-mutating analysis tool.

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?

Two concise sentences deliver the purpose, methodology, and outcome. No filler or redundant wording; the description is tightly structured and immediately useful for an agent.

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 covers what the tool does, how it does it, and what it produces. Without an output schema, it could go further in describing the exact return format, but for selection and basic invocation the details are sufficient. The absence of annotations is partially mitigated by the clear read-only analysis framing.

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 coverage is 100%, and both parameters have clear descriptions with defaults and limits. The tool description itself does not add parameter-level detail, but it references the threshold and time window conceptually. Since the schema already documents parameters well, a baseline 3 is appropriate.

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 clearly states a specific verb and resource: detecting items whose gross profit percentage has declined by more than a threshold. It further distinguishes the tool by explaining the linear regression methodology on per-item monthly GP%, which separates it from sibling detect tools like detect_customer_churn or detect_seasonality.

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 implies clear usage context: use this when you need to identify margin erosion trends on items over time. It does not explicitly name alternatives or exclusions, but the methodology and objective are specific enough that an agent can infer when it applies.

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

Deploy Server