netsuite-saved-search-mcp

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detect_anomalies

Run three anomaly checks on a NetSuite GL export: detect month gaps, ratio anomalies, and document count variances. Outputs findings with severity and supporting rows.

Instructions

Run three anomaly checks against a NetSuite GL-style export and return Findings: (1) zero_activity_period — month gaps inside the observed period range (HIGH); (2) ratio_anomaly — (account, period) total greater than 2x the account's median total across periods (MEDIUM); (3) document_count_variance — period row count more than 2 stdev from the mean across periods (MEDIUM). Each Finding includes severity, description, up to 10 supporting_rows, and total_supporting_count for the true un-truncated count. The period column should contain labels like 'Jan 2024', 'January 2024', or '2024-01'.

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": {
      "file_path": "<file_path>",
      "account_column": "<account_column>",
      "amount_column": "<amount_column>",
      "period_column": "<period_column>"
    },
    "name": "detect_anomalies"
  }
}

Input Schema

Name	Required	Description	Default
`file_path`	Yes
`account_column`	Yes
`amount_column`	Yes
`period_column`	Yes

Output Schema

Name	Required	Description	Default
`findings`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.3Jun 24, 2026

TDQS

A3.7/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It details the three anomaly checks, severity levels, and output structure (Findings with supporting_rows and total_supporting_count). It also specifies acceptable period column formats. This provides good transparency, though it doesn't mention whether the tool is read-only or has side effects.

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

Conciseness4/5

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

The description is a single paragraph that efficiently front-loads the core purpose and lists the three checks. It is concise without superfluous text, though it could benefit from bullet points for clarity.

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

Completeness3/5

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

Given no annotations and low schema coverage, the description covers the tool's functionality and output structure well. However, it omits parameter descriptions for three of four parameters, making it incomplete for an agent to correctly select and invoke the tool without additional context.

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

Parameters2/5

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

Schema description coverage is 0%, so the description should explain each parameter. It only addresses the period_column format in the last sentence, providing no guidance on file_path, account_column, or amount_column. This leaves most parameters inadequately explained.

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 the tool runs three specific anomaly checks on NetSuite GL-style exports and returns Findings. It lists each check with severity and description, making the purpose very clear and distinguishing it from sibling tools like aggregate_export or query_export.

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?

The description explains what the tool does but does not explicitly state when to use it versus alternatives like categorize_by_memo or get_headers. It implies the tool is for anomaly detection in financial data, but lacks direct guidance on prerequisites or exclusion criteria.

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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