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initialize-project-standard

Analyze user-specified projects to generate global project rules for Copilot and Cursor, optimizing development workflows on the feuse-mcp server.

Instructions

针对用户指定项目进行分析,生成Copilot 和 cursor对应的全局项目规则

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The `initProjectStandard` method defines and registers the handler for the 'initialize-project-standard' MCP tool. The handler's `execute` function constructs an XML prompt using imported `initProjectStandardPrompt` and `copilotInstructionsPrompt` templates to generate global project rules for Copilot and Cursor.
    private initProjectStandard(): void {
      this.server.addTool({
        name: 'initialize-project-standard',
        description: '针对用户指定项目进行分析,生成Copilot 和 cursor对应的全局项目规则',
        execute: async () => {
          const prompt: string = `
            <xml>
              <prompt>${initProjectStandardPrompt}</prompt>
              <template>${copilotInstructionsPrompt}</template>
            </xml>
          `
          return prompt
        },
      })
    }
  • The `registerTools` method calls `initProjectStandard()` among others to register utility tools on the MCP server.
    public registerTools(): void {
      this.apiAutomation()
      this.initProjectStandard()
      this.extractColorVars()
      this.extractSVGAssets()
      this.dowdloadSVGAssets()
    }
  • Imports the prompt template used in the tool handler from './prompt/initialize-project-standard.xml'.
    import initProjectStandardPrompt from './prompt/initialize-project-standard.xml'
  • Imports the copilot instructions template used in the tool handler.
    import copilotInstructionsPrompt from './prompt/copilot-instructions.md'
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool performs analysis and generates rules, implying a read-only or generative operation, but doesn't clarify if it modifies the project, requires specific permissions, has rate limits, or what the output format looks like. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 a single, efficient sentence in Chinese that directly states the tool's purpose without any fluff or redundancy. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly. Every word contributes to understanding the tool's function.

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 tool has 0 parameters, no annotations, and no output schema, the description is minimally adequate. It explains what the tool does but lacks details on behavioral traits, output format, or usage context. For a tool that generates rules, more information on the nature of the output would be helpful, but the simplicity of the tool (no inputs) makes the description passable as a basic overview.

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?

The input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description adds value by specifying that the tool operates on a 'user-specified project', implying some implicit input context, though it doesn't detail how the project is specified. With zero parameters, the baseline is 4, as the description provides necessary context beyond the empty schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: '针对用户指定项目进行分析,生成Copilot 和 cursor对应的全局项目规则' (Analyze a user-specified project and generate global project rules for Copilot and cursor). It specifies the verb 'analyze' and resource 'project', with the output being 'global project rules'. However, it doesn't explicitly distinguish this from sibling tools like 'Figma-To-Code' or 'similarity-figma', which may also involve project analysis but for different purposes.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, nor does it reference any sibling tools for comparison. The lack of usage context leaves the agent to infer when this tool is appropriate based solely on the purpose statement.

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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