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payroll-normalizer-mcp

payroll-normalizer-mcp

by dingxiang-me
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

standard_columns

Defines the 10 standard columns, identity types, and key distinctions (e.g., gross vs net) for social insurance payroll normalization. Must be read before column mapping.

Instructions

返回社保测算标准模板的 10 列定义、身份类型可选值与关键口径（应发≠实发等）。 模型在做列映射前应先读这个。

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": "standard_columns"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.7/5.0
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 correctly indicates this is a read-only retrieval tool returning definitions and options. No side effects are described, which is appropriate. The output schema likely covers return format, so no contradiction or gap.

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 that get straight to the point, front-loaded with purpose and usage timing. Every word adds value, no fluff.

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

Completeness5/5

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

Given no parameters, presence of output schema, and the tool's simple nature as a static reference, the description is complete. It covers purpose, usage context, and key content.

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?

There are no parameters in the schema. The rubric sets baseline 4 for 0 parameters. The description adds value by explaining what the tool returns, but no parameter-specific info is needed.

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 it returns definitions of 10 columns, identity type options, and key concepts like 应发≠实发, specifying it is a reference for column mapping. This distinguishes it from sibling tools that generate templates, inspect payroll, or normalize payroll.

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

Usage Guidelines5/5

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

The description explicitly says '模型在做列映射前应先读这个' (the model should read this before doing column mapping), providing clear guidance on when to use this tool relative to other operations.

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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