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Search biomedical literature and web content using Tavily search engine for research and clinical information.
---
name: biomedical-web-search-8ead883777fc
description: Search biomedical literature and web content using Tavily search engine for research and clinical information.
license: MIT license
metadata:
skill-author: PJLab
---
# Biomedical Web Search
## Usage
```python
import asyncio
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
class OrigeneClient:
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:
return False
async def disconnect(self):
if self.session:
await self.session_ctx.__aexit__(None, None, None)
if hasattr(self, 'transport'):
await self.transport.__aexit__(None, None, None)
def parse_result(self, result):
if isinstance(result, dict):
content_list = result.get("content") or []
else:
content_list = getattr(result, "content", []) or []
texts = []
for item in content_list:
if isinstance(item, dict):
if item.get("type") == "text":
texts.append(item.get("text") or "")
else:
if getattr(item, "type", None) == "text":
texts.append(getattr(item, "text", "") or "")
return "".join(texts)
## Initialize and use
client = OrigeneClient("https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search", "<your-api-key>")
await client.connect()
result = await client.session.call_tool("tavily_search", arguments={"query": "brain tumor"})
print(client.parse_result(result))
await client.disconnect()
```
### Tool: `tavily_search`
- Args: `query` (str) - Search query
- Returns: JSON with query, answer, results (URL, title, content, score)
### Use Cases
- Medical literature search, clinical research, disease information retrieval
---
name: biomedical-web-search-8ead883777fc
description: Search biomedical literature and web content using Tavily search engine for research and clinical information.
license: MIT license
metadata:
skill-author: PJLab
---
# Biomedical Web Search
## Usage
```python
import asyncio
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
class OrigeneClient:
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:
return False
async def disconnect(self):
if self.session:
await self.session_ctx.__aexit__(None, None, None)
if hasattr(self, 'transport'):
await self.transport.__aexit__(None, None, None)
def parse_result(self, result):
if isinstance(result, dict):
content_list = result.get("content") or []
else:
content_list = getattr(result, "content", []) or []
texts = []
for item in content_list:
if isinstance(item, dict):
if item.get("type") == "text":
texts.append(item.get("text") or "")
else:
if getattr(item, "type", None) == "text":
texts.append(getattr(item, "text", "") or "")
return "".join(texts)
## Initialize and use
client = OrigeneClient("https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search", "<your-api-key>")
await client.connect()
result = await client.session.call_tool("tavily_search", arguments={"query": "brain tumor"})
print(client.parse_result(result))
await client.disconnect()
```
### Tool: `tavily_search`
- Args: `query` (str) - Search query
- Returns: JSON with query, answer, results (URL, title, content, score)
### Use Cases
- Medical literature search, clinical research, disease information retrieval
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.