Scientific Literature Search

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. Literature Search Workflow

Search and analyze scientific literature on a research topic.

Workflow Steps:

1. Define Query - Specify research question or topic
2. Execute Search - Query scientific databases
3. Analyze Results - Extract key findings and trends

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: Research query
prompt = "Analyze the latest trends in AI research for drug discovery"

## Execute literature search
result = await client.session.call_tool(
    "FlowSearch",
    arguments={
        "prompt": prompt,
        "file_list": None
    }
)

data = client.parse_result(result)

if data.get('success'):
    print("✅ Literature search completed")
    print(f"\nResults:\n{data['result']}")
else:
    print(f"❌ Search failed: {data.get('error', 'Unknown error')}")

await client.disconnect()

Tool Descriptions

InternAgent Server:

• FlowSearch: Search and analyze scientific literature
  • Args:
    • prompt (str): Research query or question
    • file_list (list, optional): Additional files to analyze
  • Returns:
    • success (bool): Search status
    • result (str): Search results and analysis

Use Cases

• Literature review for research papers
• Trend analysis in scientific fields
• Systematic literature searches
• Citation and reference discovery
• Research gap identification

Performance Notes

• Execution time: 10-60 seconds depending on query complexity
• Data sources: Multiple scientific databases
• Output: Comprehensive analysis with key findings