mcp-ClinicalTrial

Instructions

Implementation Reference

• src/index.ts:708-821 (handler)

  The `aePipelineRag` method handles the full RAG pipeline logic for adverse events, including data fetching, chunking, keyword retrieval, and summary generation.

  private async aePipelineRag(params: AEPipelineRAGParams): Promise<{ content: Array<{ type: string; text: string }> }> {
    try {
      // 1. 构建搜索参数
      const searchParams: ClinicalTrialSearchParams = {
        pageSize: params.filters?.limit || 50,
        countTotal: true
      };
  
      if (params.drug) searchParams.intervention = params.drug;
      if (params.condition) searchParams.condition = params.condition;
      if (params.filters?.status) searchParams.status = params.filters.status;
      if (params.filters?.include_completed_only) {
        searchParams.status = "COMPLETED";
      }
  
      // 2. 抓取数据
      const data = await this.makeRequest(searchParams);
      
      if (!data.studies || data.studies.length === 0) {
        const result: RAGResult = {
          source: "clinicaltrials",
          query: params.query,
          drug: params.drug,
          condition: params.condition,
          top_chunks: [],
          summary: "未找到匹配的临床试验数据。请尝试调整搜索条件。",
          citations: []
        };
        
        return {
          content: [{
            type: "text",
            text: JSON.stringify(result, null, 2)
          }]
        };
      }
  
      // 3. 提取和分块文本
      const allChunks: TextChunk[] = [];
      
      for (const study of data.studies) {
        const studyText = this.extractStudyText(study);
        if (studyText.trim().length > 0) {
          const nctId = study.protocolSection?.identificationModule?.nctId || 'unknown';
          const title = study.protocolSection?.identificationModule?.briefTitle || 'Untitled Study';
          
          const chunks = chunkText(
            studyText,
            1000,
            200,
            nctId,
            {
              title,
              type: 'clinical_trial',
              hasResults: !!study.resultsSection,
              hasAdverseEvents: !!study.resultsSection?.adverseEventsModule
            }
          );
          
          allChunks.push(...chunks);
        }
      }
  
      // 4. 构建查询关键词
      const queryText = [params.query, params.drug, params.condition]
        .filter(Boolean)
        .join(' ');
      
      const extraKeywords = [
        'adverse events', 'side effects', 'safety', 'toxicity',
        'placebo', 'control', 'comparison', 'risk',
        '不良事件', '副作用', '安全性', '对照'
      ];
  
      // 5. 检索和排序
      const topChunks = rankAndPickTop(
        allChunks,
        queryText,
        params.top_k,
        extraKeywords
      );
  
      // 6. 生成摘要
      const summary = summarizeChunks(topChunks, {
        source: 'clinicaltrials',
        query: params.query,
        drug: params.drug,
        condition: params.condition,
        maxLength: 1200
      });
  
      // 7. 提取引用
      const citations = extractCitations(topChunks);
  
      // 8. 构建结果
      const result: RAGResult = {
        source: "clinicaltrials",
        query: params.query,
        drug: params.drug,
        condition: params.condition,
        top_chunks: topChunks.map(chunk => ({
          ...chunk,
          text: chunk.text.length > 1200 ? chunk.text.slice(0, 1200) + '...' : chunk.text
        })),
        summary,
        citations
      };
  
      return {
        content: [{
          type: "text",
          text: JSON.stringify(result, null, 2)
        }]
      };

• src/index.ts:46-56 (schema)

  The `AEPipelineRAGParamsSchema` Zod schema defines the input parameters for the `ae_pipeline_rag` tool.

  const AEPipelineRAGParamsSchema = z.object({
    query: z.string().optional(),
    drug: z.string().optional(),
    condition: z.string().optional(),
    top_k: z.coerce.number().int().min(1).max(10).optional().default(5),
    filters: z.object({
      status: z.string().optional().default("COMPLETED"),
      limit: z.coerce.number().int().min(1).max(100).optional().default(50),
      include_completed_only: z.boolean().optional().default(true)
    }).optional().default({})
  });

• src/index.ts:197-247 (registration)

  The tool `ae_pipeline_rag` is defined in the `ListToolsRequestSchema` response.

  {
    name: "ae_pipeline_rag",
    description: "Advanced RAG pipeline for adverse events analysis. Fetches, extracts, chunks, retrieves and summarizes clinical trial data in one call to prevent LLM response truncation.",
    inputSchema: {
      type: "object",
      properties: {
        query: {
          type: "string",
          description: "Natural language query about adverse events. Example: 'gastrointestinal bleeding risk vs placebo'"
        },
        drug: {
          type: "string",
          description: "Drug name to focus the analysis on. Example: 'Vemurafenib'"
        },
        condition: {
          type: "string",
          description: "Medical condition context. Example: 'melanoma', 'cancer'"
        },
        top_k: {
          type: "number",
          description: "Number of most relevant text chunks to return (1-10)",
          default: 5,
          minimum: 1,
          maximum: 10
        },
        filters: {
          type: "object",
          description: "Additional filters for data retrieval",
          properties: {
            status: {
              type: "string",
              description: "Study status filter",
              default: "COMPLETED"
            },
            limit: {
              type: "number",
              description: "Maximum studies to fetch",
              default: 50,
              minimum: 1,
              maximum: 100
            },
            include_completed_only: {
              type: "boolean",
              description: "Only include completed studies with results",
              default: true
            }
          }
        }
      }
    }
  },

• src/index.ts:322-324 (registration)

  The `ae_pipeline_rag` tool request is handled in the `CallToolRequestSchema` switch statement, which calls `this.aePipelineRag`.

  case "ae_pipeline_rag":
    const ragParams = AEPipelineRAGParamsSchema.parse(args);
    return await this.aePipelineRag(ragParams);