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WaHaiLong

Kingdee MCP Server

by WaHaiLong

kingdee_query_cost_calculation

Read-onlyIdempotent

Query cost calculation bills by year, period, or material. Retrieve fields like cost amount, material cost, and labour cost.

Instructions

查询成本计算单(CB_CostCalBill)列表。

常用 filter_string:

  • 指定年度: "FYear=2024"

  • 指定期间: "FPeriod='01'"

  • 指定物料: "FMaterialId.FNumber='MAT001'"

推荐 field_keys: FYear,FPeriod,FMaterialId.FNumber,FMaterialId.FName,FCostAmt,FMaterialCost,FLabourCost

Returns: str: JSON 格式的成本计算单列表

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds that it returns a JSON list, which is minimal. No additional behavioral traits like pagination behavior (though schema covers start_row/limit) or API limits are mentioned. No contradiction with annotations.

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 very concise: one line for purpose, then bullet-like examples for filter_string, a line for recommended field_keys, and a line for return type. Every sentence adds value with no redundancy. Front-loaded with purpose.

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 complexity (6 sub-parameters, many siblings), the description covers purpose, usage examples, and return format well. It could mention the expected form_id value (CB_CostCalBill) explicitly, but the name implies it. The presence of an output schema (inferred from return type) reduces need for further detail.

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?

Despite schema description coverage reported as 0%, the schema actually has descriptions on each property. The tool description adds domain-specific examples for filter_string (FYear, FPeriod, FMaterialId.FNumber) and recommends field_keys, which go beyond the generic schema descriptions and provide practical guidance.

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 queries a list of cost calculation sheets (CB_CostCalBill), which is a specific entity. The title from annotations matches the description. Among many query siblings, this one is uniquely identified by its focus on cost calculation.

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?

Provides concrete filter_string examples (year, period, material) and recommends field_keys. However, it does not explicitly state when to use this tool versus other cost-related queries like kingdee_query_material_cost or kingdee_query_cost_adjustments, nor does it mention not to use it for other cost forms.

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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