Note: If you have mcporter installed locally, you should use mcporter to invoke the MCP tool instead of directly calling the MCP tool. The mcporter CLI provides a more convenient interface and better error handling.

Quick Fix Suggestion via Gitee MCP

Analyze a Gitee Issue and generate intelligent fix suggestions including:

• Identification of files that need to be modified
• Code location hints (line numbers, function names)
• Draft implementation code snippets
• Step-by-step implementation plan

Prerequisites

• Gitee MCP Server configured (tools: get_repo_issue_detail, list_issue_comments, search_files_by_content, get_file_content)
• Must be executed in a local clone of the repository
• User must provide: repository owner, repository name, Issue number

Local-First Search Strategy

Why Local-First?

This skill prioritizes local file operations over Gitee API for code search:

• Speed: Local searches complete in milliseconds vs. seconds for API calls
• No Rate Limits: No 100 requests/minute restriction
• Real-time: Searches include uncommitted local changes
• Cost: Zero API usage costs

Tool Selection Priority

Priority	Tool	Availability
1st	glob, grep (built-in)	100%
2nd	ast_grep_search, lsp_find_references	100%
3rd	Git commands (via bash)	~90%
Last	Gitee API (search_files_by_content)	Fallback only

Steps

Step 1: Fetch Issue Details

Use get_repo_issue_detail to retrieve full Issue information:

• Title and description
• Labels (bug/feature/enhancement)
• Existing comments

Step 2: Analyze Issue Content

Identify Issue Type:

• bug: error, crash, broken, not working, 错误, 失败
• feature: add, support, implement, new, 新增, 添加
• enhancement: improve, optimize, 改进, 优化

Extract Code Entities:

• Function names (in backticks or patterns)
• Class names
• File names
• Error messages / stack traces

Step 3: Search Code (Local-First)

Use built-in tools in priority order:

// 1. File name search (fastest)
glob(`**/*${keyword}*.{js,ts,py,go}`, { cwd: localRepoPath })

// 2. Content search
grep({
  pattern: `function\\s+${funcName}`,
  path: localRepoPath,
  output_mode: "content"
})

// 3. AST search (semantic)
ast_grep_search({
  pattern: "function $NAME($$$) { $$$ }",
  lang: "javascript",
  paths: [localRepoPath]
})

// 4. Fallback: Remote search (max 3 calls)
if (localResults.length < 3) {
  search_files_by_content({ owner, repo, query: keyword, limit: 10 })
}

Step 4: Read and Analyze Key Files

Read each candidate file to:

• Identify relevant functions/classes
• Understand code structure
• Assess how the Issue relates to this file

Step 5: Generate Fix Suggestions

Present results in this format:

## Issue #{number}: {title}

**Type**: {bug/feature} | **Confidence**: {score}% | **Complexity**: {low/medium/high}

### Primary Files (Must Modify)

**1. `{file_path}`** [Confidence: XX%]
- **Change**: {modify/add/delete}
- **Location**: Around line {X}

<details>
<summary>Code Draft</summary>

```{language}
// Current:
{existing_code}

// Suggested:
{suggested_code}

Secondary Files (May Need Changes)

• {file_path}: {reason}

Implementation Plan

1. {step 1} - File: {file}, ~{time}
2. {step 2} - File: {file}, ~{time}

Testing Suggestions

• {test suggestion}

Questions for You

1. {uncertainty_question}

### Step 6: Handle User Response

- **Approve**: Summarize and transition to `implement-issue` skill
- **Adjust**: Re-analyze with new information
- **Search More**: Expand search scope
- **Cancel**: Offer other help

## Notes

- Always indicate confidence level - ask for clarification when uncertain
- Follow existing code patterns when suggesting changes
- Warn immediately if Issue mentions credentials/secrets
- If unable to locate relevant code, ask for more context rather than guessing

## References

- [Tool Reference Guide](references/TOOL_REFERENCE.md) - Detailed search patterns and tool usage