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Kingdee MCP Server

Kingdee MCP Server

by WaHaiLong
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

kingdee_query_production_report

Read-only

Query production reports to retrieve completed quantities, working hours, and yield rates for tracking manufacturing progress.

Instructions

查询生产汇报单（PRD_MOReport）。

生产汇报记录工序完成数量/工时/良品率等信息，是生产入库的提前工序。

Returns: str: JSON 数组，含 FBillNo/FMOId/FMaterialId/FReportQty/FHourQty 等。

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_report"
  }
}

Input Schema

NameRequiredDescriptionDefault
paramsYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds useful context about the data content (工序完成数量/工时/良品率) and return fields, but does not disclose additional behavioral traits such as pagination, rate limits, or performance. This adds some value beyond annotations, hence a 3.

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 concise: two lines for purpose and context, plus a return format line. Every sentence adds value. However, it could be slightly more structured, e.g., explicitly listing parameters, but overall efficient.

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

Completeness2/5

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

Despite having an output schema (not shown) and return fields listed, the description lacks guidance on constructing the filter_string or other parameters. For a tool with three parameters and 0% schema coverage, the description should compensate but does not. The return fields help but incomplete elsewhere.

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

Parameters1/5

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

Schema description coverage is 0% (no parameter descriptions in schema), and the tool description does not mention any parameters or provide usage guidance for filter_string, top, or orderby. The agent must rely solely on the schema, which lacks descriptions. This is a critical gap for a query tool.

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 verb '查询' (query) and the resource '生产汇报单（PRD_MOReport）', distinguishing it from sibling tools like kingdee_query_production_orders and kingdee_query_production_plan. It also explains the report's content and its role as a preliminary step for production warehousing.

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

Usage Guidelines2/5

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

The description does not provide explicit guidance on when to use this tool versus alternatives. It mentions the report is a prerequisite for production warehousing, but no when-not-to-use or direct comparisons to siblings. The agent must infer usage from the tool name and context.

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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