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Kingdee MCP Server

Kingdee MCP Server

by WaHaiLong
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

kingdee_query_production_pick_materials

Read-onlyIdempotent

Retrieve production pick materials (PRD_PickMtrl) records with filters for document status, production order, or warehouse. Outputs JSON list for inventory management.

Instructions

查询生产领料单（PRD_PickMtrl）列表。

常用 filter_string：

  • 已审核: "FDocumentStatus='C'"

  • 指定生产订单: "FMoBillNo='MO000001'"

  • 指定仓库: "FStockId.FNumber='CK001'"

推荐 field_keys： FID,FBillNo,FDate,FDocumentStatus,FMoBillNo,FMaterialId.FName,FPickQty,FStockId.FName

Returns: str: JSON 格式的生产领料单列表

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": {
      "params": "<params>"
    },
    "name": "kingdee_query_production_pick_materials"
  }
}

Input Schema

NameRequiredDescriptionDefault
paramsYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description adds minimal value beyond stating it returns a JSON list and explaining filter_string syntax. No additional behavioral traits (e.g., permissions, side effects) are disclosed.

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 front-loaded with the main purpose and includes helpful code-like examples. It is reasonably concise, though the list of filter_string examples and field_keys could be more compact.

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 the annotations and schema, the description is adequate for a basic query tool. It specifies the form ID and return format, but does not mention pagination defaults (e.g., limit=20) or that filter_string and field_keys are optional, though the schema covers these.

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?

The schema covers all parameters with descriptions, so baseline is 3. The tool description adds examples for filter_string and recommended field_keys, but does not explain other parameters like order_string, start_row, or limit beyond what the schema provides.

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 production pick materials (PRD_PickMtrl) list, providing examples of filter_string and recommended field_keys. It is distinct from sibling query tools like kingdee_query_bills or kingdee_query_production_orders.

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 provides concrete examples of filter_string for common scenarios (approved, specific order, specific warehouse) and recommends field_keys, but does not explicitly state when to use this tool versus alternatives or provide 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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