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payroll-normalizer-mcp

payroll-normalizer-mcp

by dingxiang-me
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

inspect_payroll

Inspects payroll spreadsheets in a folder, returning headers, sample rows, and detected field mappings to identify issues or guide manual overrides for normalization.

Instructions

检查文件夹内每个工资表(.xlsx/.xls/.csv)：返回表头、前3行样本、自动识别到的字段映射、应发口径与问题。 当某文件无法自动映射时，模型据此判断每列含义，再在 normalize_payroll 的 overrides 里给出 column_map/entity/ym。

folder: 存放工资表的文件夹绝对路径。

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": {
      "folder": "<folder>"
    },
    "name": "inspect_payroll"
  }
}

Input Schema

NameRequiredDescriptionDefault
folderYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior3/5

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

Without annotations, the description carries the transparency burden. It discloses the tool's outputs and handling of unmapped files, but does not explicitly state side effects, auth needs, or confirm read-only nature, leaving some ambiguity.

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, front-loading the action and outputs, with no fluff. Every sentence adds value.

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 key outputs and fallback behavior, sufficient for the tool's complexity. Given an output schema exists, it does not need to detail return values, but could mention the output schema structure.

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

Parameters4/5

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

With 0% schema coverage, the description clarifies 'folder' as an absolute path, which adds meaning beyond the name. The single parameter is adequately 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 that the tool inspects payroll files in a folder and returns headers, sample rows, field mapping, and issues. It also ties into the workflow with sibling tool normalize_payroll, establishing its distinct role.

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 usage when needing to inspect payroll files and map columns, and mentions fallback behavior for unmapped files. It does not explicitly state when not to use it, but the context with siblings is clear.

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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