meta-analysis-execution

# Meta-Analysis Execution ## Usage ### 1. MCP Server Definition ```python import asyncio import json from mcp.client.streamable_http import streamablehttp_client from mcp import ClientSession class InternAgentClient: """InternAgent MCP Client""" def __init__(self, server_url: str, api_key: str): self.server_url = server_url self.api_key = api_key self.session = None async def connect(self): try: self.transport = streamablehttp_client( url=self.server_url, headers={"SCP-HUB-API-KEY": self.api_key} ) self.read, self.write, self.get_session_id = await self.transport.__aenter__() self.session_ctx = ClientSession(self.read, self.write) self.session = await self.session_ctx.__aenter__() await self.session.initialize() return True except Exception as e: print(f"✗ connect failure: {e}") return False async def disconnect(self): try: if self.session: await self.session_ctx.__aexit__(None, None, None) if hasattr(self, 'transport'): await self.transport.__aexit__(None, None, None) except Exception as e: print(f"✗ disconnect error: {e}") def parse_result(self, result): try: if hasattr(result, 'content') and result.content: content = result.content[0] if hasattr(content, 'text'): return json.loads(content.text) return str(result) except Exception as e: return {"error": f"parse error: {e}", "raw": str(result)} ``` ### 2. Meta-Analysis Workflow Synthesize multiple studies to generate comprehensive research insights. **Workflow Steps:** 1. **Define Research Question** - Specify meta-analysis objective 2. **Execute Analysis** - Process multiple studies systematically 3. **Generate Report** - Create summary tables or comprehensive reports **Implementation:** ```python ## Initialize client client = InternAgentClient( "https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent", "<your-api-key>" ) if not await client.connect(): print("connection failed") exit() ## Input: Meta-analysis query prompt = "Analyze the effectiveness of mRNA vaccines against COVID-19" report_type = "table" # or "comprehensive" ## Execute meta-analysis result = await client.session.call_tool( "MetaAnalysis", arguments={ "prompt": prompt, "file_list": None, "type": report_type } ) data = client.parse_result(result) if 'final_report' in data: print("✅ Meta-analysis completed") print(f"Task ID: {data.get('task_id', 'N/A')}") final_report = data['final_report'] print(f"\nReport Type: {final_report.get('type', 'N/A')}") print(f"\nContent:\n{final_report.get('content', 'N/A')}") else: print(f"❌ Analysis failed: {data.get('error', 'Unknown error')}") await client.disconnect() ``` ### Tool Descriptions **InternAgent Server:** - `MetaAnalysis`: Perform meta-analysis on research studies - Args: - `prompt` (str): Research question for meta-analysis - `file_list` (list, optional): Additional study files - `type` (str): Output format ("table" or "comprehensive") - Returns: - `task_id` (str): Analysis task identifier - `final_report` (dict): Meta-analysis results - `type` (str): Report format - `content` (str): Analysis findings ### Input/Output **Input:** - `prompt`: Research question or hypothesis - `type`: Report format (table for structured data, comprehensive for detailed analysis) - `file_list`: Optional list of study files to include **Output:** - Structured report with: - Study summaries - Effect sizes and confidence intervals - Statistical heterogeneity metrics - Summary conclusions ### Use Cases - Systematic reviews of clinical trials - Evidence synthesis in medicine - Research effectiveness evaluation - Policy decision support - Academic literature reviews ### Performance Notes - **Execution time**: 1-5 minutes depending on number of studies - **Output formats**: Markdown tables or comprehensive text reports - **Data quality**: Automatically assesses study quality indicators