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Kingdee MCP Server

Kingdee MCP Server

by WaHaiLong
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

kingdee_query_materials

Read-onlyIdempotent

Query material master data from Kingdee Cloud Star ERP using filters like name or number. Returns details including code, name, specification, unit, and material group.

Instructions

查询物料基础资料（BD_Material）。

常用 filter_string：

  • 按编码前缀: "FNumber like 'HG%'"

  • 按名称模糊: "FName like '%钢板%'"

  • 已审核启用: "FDocumentStatus='C'"

Returns: str: JSON 格式的物料列表，含编码、名称、规格、单位、物料分组

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_materials"
  }
}

Input Schema

NameRequiredDescriptionDefault
paramsYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds value by specifying the return format as a JSON string with fields like code, name, specification, unit, and material group. This aligns with annotations and provides useful behavioral context beyond the structured hints.

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 concise: a brief purpose statement, three filter examples, and a return specification. Every sentence adds value without redundancy. It is front-loaded with the core purpose and uses bullet points for clarity.

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 tool's complexity (4 parameters) and the presence of an output schema (which handles return values), the description covers the essential usage pattern with filter examples. However, it omits details on pagination (start_row, limit) and custom fields (field_keys). The query examples provide a good starting point but not full completeness.

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?

Schema description coverage is 0%, meaning the schema's own descriptions are insufficient. The description partially compensates by illustrating filter_string usage with examples. However, it does not explain the other parameters (field_keys, start_row, limit), which are crucial for customization and pagination. The examples for filter_string are helpful but leave gaps.

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 material basic data (BD_Material) with a specific verb '查询' (query) and resource. It distinguishes from sibling query tools like kingdee_query_bills or kingdee_query_inventory by focusing on materials. Examples of filter strings further clarify the purpose.

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 provides common filter_string examples, guiding usage through specific filters like code prefix and name fuzzy search. It does not explicitly state when not to use this tool or mention alternatives, but the context of sibling tools implies material-specific queries. This is clear but lacks exclusion guidance.

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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