Meta-Analysis Execution

Usage

1. MCP Server Definition

import asyncio
import json
from contextlib import AsyncExitStack
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._stack = AsyncExitStack()
            await self._stack.__aenter__()
            self.read, self.write, self.get_session_id = await self._stack.enter_async_context(self.transport)
            self.session_ctx = ClientSession(self.read, self.write)
            self.session = await self._stack.enter_async_context(self.session_ctx)
            await self.session.initialize()
            return True
        except Exception as e:
            print(f"✗ connect failure: {e}")
            return False

    async def disconnect(self):
        """Disconnect from server"""
        try:
            if hasattr(self, '_stack'):
                await self._stack.aclose()
            print("✓ already disconnect")
        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:

## 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