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chanshawoh

yudao-pilot-mcp

by chanshawoh

inspect_codegen_context_tool

Builds the code generation context by merging configuration rules, backend defaults, and SQL menu data for a given table.

Instructions

结合配置规则、后端默认配置和 SQL 菜单数据,构建生成代码所需上下文。

backend_module_dir: 显式后端目标模块目录,支持 yudao-module-a/yudao-module-b 或 a/b。 backend_package_module: 显式 Java package module 名,例如 b;未传时使用 module_name。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
menu_nameNo
table_nameYes
entity_nameNo
module_nameNo
business_nameNo
parent_menu_idNo
workspace_rootNo
parent_menu_nameNo
backend_module_dirNo
backend_package_moduleNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses that the tool combines configuration rules and defaults to build context, suggesting a read-only operation, but it does not explicitly state if it has side effects or if it modifies any state. A score of 3 reflects this partial transparency.

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 compact with three sentences: the first defines the overall purpose, the next two detail specific parameters. It is front-loaded and efficient, though it could include more parameter information without becoming verbose.

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?

Given the tool has 10 parameters, no schema descriptions, and no annotations, the description is insufficient. It only covers two parameters and does not explain required inputs like table_name or optional ones like entity_name, menu_name, etc. An output schema exists but does not compensate for missing input guidance.

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

Parameters2/5

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

With 0% schema description coverage, the description is the only source. It explains only 2 out of 10 parameters (backend_module_dir and backend_package_module) with some detail. The remaining 8 parameters are not described at all, leaving significant ambiguity.

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 builds context for code generation by combining config rules, backend defaults, and SQL menu data. This verb-resource combination is specific and distinct from sibling tools like infer_codegen_plan_tool.

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 explains two parameters (backend_module_dir and backend_package_module) in detail, implying they are optional. However, it does not explicitly state when to use this tool versus alternatives like infer_codegen_plan_tool, nor specify prerequisites or exclusions.

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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