Gitea MCP Tool

Instructions

Implementation Reference

• src/tools/workflow.ts:496-587 (handler)

  Main handler function for 'gitea_workflow_infer_labels' tool. Loads workflow config, fetches the issue, uses LabelInferenceEngine to infer recommended labels, and optionally applies them via Gitea API.
  export async function workflowInferLabels( ctx: WorkflowToolsContext, args: { owner?: string; repo?: string; config?: WorkflowConfig; issue_number: number; auto_apply?: boolean; } ): Promise<{ success: boolean; issue_number: number; inferred_labels: Array<{ label: string; confidence: number; reason: string }>; applied_labels?: string[]; error?: string; }> { logger.debug({ args: { ...args, config: args.config ? '[provided]' : undefined } }, 'Inferring labels'); const { owner, repo } = ctx.contextManager.resolveOwnerRepo(args.owner, args.repo); const autoApply = args.auto_apply ?? false; // 获取配置 let config = args.config; if (!config) { const loadResult = await workflowLoadConfig(ctx, { owner, repo }); if (!loadResult.success || !loadResult.config) { return { success: false, issue_number: args.issue_number, inferred_labels: [], error: loadResult.error || '无法加载配置', }; } config = loadResult.config; } try { // 获取 Issue const issue = await ctx.client.get<Issue>(`/repos/${owner}/${repo}/issues/${args.issue_number}`); // 推断标签 const inferenceEngine = new LabelInferenceEngine(config); const recommendedLabels = inferenceEngine.getRecommendedLabels(issue); const appliedLabels: string[] = []; // 自动应用标签 if (autoApply && recommendedLabels.length > 0) { // 获取仓库的标签 ID 映射 const repoLabels = await ctx.client.get<Array<{ id: number; name: string }>>( `/repos/${owner}/${repo}/labels` ); const labelIdMap = new Map(repoLabels.map((l) => [l.name, l.id])); for (const item of recommendedLabels) { const labelId = labelIdMap.get(item.label); if (labelId) { try { await ctx.client.post(`/repos/${owner}/${repo}/issues/${args.issue_number}/labels`, { labels: [labelId], }); appliedLabels.push(item.label); } catch (error) { logger.warn({ label: item.label, error }, 'Failed to apply label'); } } } } logger.info( { owner, repo, issue_number: args.issue_number, inferred: recommendedLabels.length, applied: appliedLabels.length }, 'Labels inferred' ); return { success: true, issue_number: args.issue_number, inferred_labels: recommendedLabels, applied_labels: autoApply ? appliedLabels : undefined, }; } catch (error) { const errorMessage = error instanceof Error ? error.message : String(error); logger.error({ owner, repo, issue_number: args.issue_number, error: errorMessage }, 'Failed to infer labels'); return { success: false, issue_number: args.issue_number, inferred_labels: [], error: errorMessage, }; } }
• src/tools-registry/workflow-registry.ts:187-217 (registration)

  Registers the 'gitea_workflow_infer_labels' MCP tool with title, description, input schema (Zod), and wrapper handler that calls the main workflowInferLabels function.
  mcpServer.registerTool( 'gitea_workflow_infer_labels', { title: '智能标签推断', description: 'Infer labels for an issue based on title and body content. Uses keyword matching and pattern recognition to suggest type, priority, and area labels.', inputSchema: z.object({ owner: z.string().optional().describe('Repository owner. Uses context if not provided'), repo: z.string().optional().describe('Repository name. Uses context if not provided'), issue_number: z.number().describe('Issue number to analyze'), auto_apply: z.boolean().optional().describe('Automatically apply inferred labels (default: false)'), }), }, async (args) => { try { const result = await WorkflowTools.workflowInferLabels( { client: ctx.client, contextManager: ctx.contextManager }, args ); return { content: [{ type: 'text' as const, text: JSON.stringify(result, null, 2) }], isError: !result.success, }; } catch (error: unknown) { const errorMessage = error instanceof Error ? error.message : String(error); return { content: [{ type: 'text' as const, text: `Error: ${errorMessage}` }], isError: true, }; } }
• src/utils/label-inference.ts:40-324 (helper)

  Core LabelInferenceEngine class used by the handler for inferring labels from issue title/body using keyword matching, patterns, title prefixes, and config-defined rules. Provides getRecommendedLabels() called in handler.
  export class LabelInferenceEngine { private config: WorkflowConfig; constructor(config: WorkflowConfig) { this.config = config; } /** * 对 Issue 进行完整的标签推断 */ inferAll(issue: Issue): LabelInferenceResult { const typeResult = this.inferType(issue); const priorityResult = this.inferPriority(issue, typeResult?.value); const areaResults = this.inferAreas(issue); // 收集所有推断结果 const all: Array<{ label: string; confidence: number; reason: string }> = []; const prefixes = getLabelPrefixes(this.config); if (typeResult && typeResult.confidence >= this.getConfidenceThreshold()) { all.push({ label: `${prefixes.type}${typeResult.value}`, confidence: typeResult.confidence, reason: typeResult.reason, }); } if (priorityResult && priorityResult.confidence >= this.getConfidenceThreshold()) { all.push({ label: `${prefixes.priority}${priorityResult.value}`, confidence: priorityResult.confidence, reason: priorityResult.reason, }); } for (const area of areaResults) { if (area.confidence >= this.getConfidenceThreshold()) { all.push({ label: `${prefixes.area}${area.value}`, confidence: area.confidence, reason: area.reason, }); } } return { type: typeResult, priority: priorityResult, areas: areaResults, all, }; } /** * 推断 Issue 类型 */ inferType(issue: Issue): InferenceResult<string> | null { const text = this.normalizeText(issue.title + ' ' + issue.body); const keywords = this.config.automation.label_inference.type_keywords; let bestMatch: InferenceResult<string> | null = null; let maxScore = 0; for (const [type, typeKeywords] of Object.entries(keywords)) { const { score, matchedKeywords } = this.calculateKeywordScore(text, typeKeywords); if (score > maxScore) { maxScore = score; bestMatch = { value: type, confidence: Math.min(score, 1.0), reason: `匹配关键词: ${matchedKeywords.join(', ')}`, }; } } // 如果没有匹配到，尝试从标题前缀推断 if (!bestMatch || maxScore < 0.3) { const prefixMatch = this.inferTypeFromPrefix(issue.title); if (prefixMatch) { return prefixMatch; } } return bestMatch; } /** * 推断 Issue 优先级 */ inferPriority(issue: Issue, inferredType?: string): InferenceResult<string> | null { const text = this.normalizeText(issue.title + ' ' + issue.body); const keywords = this.config.automation.label_inference.priority_keywords; // 安全问题强制 P0 if (inferredType === 'security') { return { value: 'P0', confidence: 1.0, reason: '安全问题自动标记为紧急', }; } // 根据关键词匹配 for (const [priority, priorityKeywords] of Object.entries(keywords)) { const { score, matchedKeywords } = this.calculateKeywordScore(text, priorityKeywords); if (score >= 0.3) { return { value: priority, confidence: Math.min(score + 0.2, 1.0), reason: `匹配关键词: ${matchedKeywords.join(', ')}`, }; } } // 默认优先级 const defaultPriority = this.config.automation.new_issue_defaults.default_priority; return { value: defaultPriority, confidence: 0.5, reason: '使用默认优先级', }; } /** * 推断领域标签 */ inferAreas(issue: Issue): InferenceResult<string>[] { const results: InferenceResult<string>[] = []; const text = this.normalizeText(issue.title + ' ' + issue.body); const patterns = this.config.automation.label_inference.area_patterns; if (!patterns) { return results; } for (const [area, areaPatterns] of Object.entries(patterns)) { for (const pattern of areaPatterns) { if (text.includes(pattern.toLowerCase())) { results.push({ value: area, confidence: 0.8, reason: `匹配路径模式: ${pattern}`, }); break; // 每个领域只匹配一次 } } } // 根据关键词推断领域 const areaKeywords: Record<string, string[]> = { api: ['api', 'endpoint', '接口', 'rest', 'graphql', 'grpc'], database: ['database', 'db', '数据库', 'sql', 'migration', 'model', '模型'], auth: ['auth', 'authentication', 'authorization', '认证', '授权', 'login', 'token', 'jwt'], ui: ['ui', 'component', '组件', 'button', 'form', 'modal', '界面'], performance: ['performance', '性能', 'slow', '慢', 'optimize', '优化', 'memory', 'cpu'], }; for (const [area, keywords] of Object.entries(areaKeywords)) { // 只推断配置中存在的领域 if (!this.config.labels.area || !this.config.labels.area[area]) { continue; } const { score, matchedKeywords } = this.calculateKeywordScore(text, keywords); if (score >= 0.3 && !results.some((r) => r.value === area)) { results.push({ value: area, confidence: score, reason: `匹配关键词: ${matchedKeywords.join(', ')}`, }); } } return results; } /** * 检查 Issue 是否缺少必要标签 */ checkMissingLabels(issue: Issue): string[] { const missing: string[] = []; const existingLabels = issue.labels.map((l) => l.name); const prefixes = getLabelPrefixes(this.config); // 检查类型标签 const hasTypeLabel = existingLabels.some((l) => matchLabel(prefixes.type, l) !== null); if (this.config.automation.new_issue_defaults.require_type_label && !hasTypeLabel) { missing.push('type'); } // 检查优先级标签 const hasPriorityLabel = existingLabels.some((l) => matchLabel(prefixes.priority, l) !== null); if (this.config.automation.new_issue_defaults.require_priority_label && !hasPriorityLabel) { missing.push('priority'); } // 检查状态标签 const hasStatusLabel = existingLabels.some((l) => matchLabel(prefixes.status, l) !== null); if (!hasStatusLabel) { missing.push('status'); } return missing; } /** * 获取推荐添加的标签 */ getRecommendedLabels(issue: Issue): Array<{ label: string; confidence: number; reason: string }> { const existingLabels = new Set(issue.labels.map((l) => l.name)); const inference = this.inferAll(issue); return inference.all.filter((item) => !existingLabels.has(item.label)); } // ============ 私有方法 ============ private normalizeText(text: string): string { return text.toLowerCase().trim(); } private getConfidenceThreshold(): number { return this.config.automation.label_inference.confidence_threshold; } private calculateKeywordScore( text: string, keywords: string[] ): { score: number; matchedKeywords: string[] } { const matchedKeywords: string[] = []; let matchCount = 0; for (const keyword of keywords) { const normalizedKeyword = keyword.toLowerCase(); // 使用词边界匹配，避免部分匹配 const regex = new RegExp(`\\b${this.escapeRegex(normalizedKeyword)}\\b|${this.escapeRegex(normalizedKeyword)}`, 'gi'); const matches = text.match(regex); if (matches) { matchCount += matches.length; matchedKeywords.push(keyword); } } // 计算分数：匹配数量 / 关键词数量，加权处理 const baseScore = matchedKeywords.length / keywords.length; const bonusScore = Math.min(matchCount * 0.1, 0.3); // 多次匹配加分 return { score: Math.min(baseScore + bonusScore, 1.0), matchedKeywords, }; } private escapeRegex(str: string): string { return str.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); } private inferTypeFromPrefix(title: string): InferenceResult<string> | null { const prefixPatterns: Array<{ pattern: RegExp; type: string }> = [ { pattern: /^(fix|bug|修复|修正)[\s:：]/i, type: 'bug' }, { pattern: /^(feat|feature|新增|添加|实现)[\s:：]/i, type: 'feature' }, { pattern: /^(docs|文档|doc)[\s:：]/i, type: 'docs' }, { pattern: /^(refactor|重构)[\s:：]/i, type: 'refactor' }, { pattern: /^(test|测试)[\s:：]/i, type: 'test' }, { pattern: /^(security|安全)[\s:：]/i, type: 'security' }, { pattern: /^(chore|杂项)[\s:：]/i, type: 'refactor' }, { pattern: /^(perf|性能)[\s:：]/i, type: 'refactor' }, ]; for (const { pattern, type } of prefixPatterns) { if (pattern.test(title)) { return { value: type, confidence: 0.9, reason: `标题前缀匹配: ${pattern.source}`, }; } } return null; } }
• src/tools-registry/workflow-registry.ts:193-198 (schema)

  Zod input schema defining the tool parameters: owner/repo (optional), required issue_number, optional auto_apply.
  inputSchema: z.object({ owner: z.string().optional().describe('Repository owner. Uses context if not provided'), repo: z.string().optional().describe('Repository name. Uses context if not provided'), issue_number: z.number().describe('Issue number to analyze'), auto_apply: z.boolean().optional().describe('Automatically apply inferred labels (default: false)'), }),