---
name: web-literature-mining-bbbd935f0316
description: "Scientific Literature Mining - Mine scientific literature: PubMed search, arXiv search, web search, and Tavily deep search. Use this skill for scientific informatics tasks involving pubmed search search literature search web tavily search. Combines 4 tools from 2 SCP server(s)."
---

# Scientific Literature Mining

**Discipline**: Scientific Informatics | **Tools Used**: 4 | **Servers**: 2

## Description

Mine scientific literature: PubMed search, arXiv search, web search, and Tavily deep search.

## Tools Used

- **`pubmed_search`** from `search-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search`
- **`search_literature`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory`
- **`search_web`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory`
- **`tavily_search`** from `search-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search`

## Workflow

1. Search PubMed
2. Search arXiv
3. Web search
4. Tavily deep search

## Test Case

### Input
```json
{
    "query": "CRISPR Cas9 gene therapy 2024"
}
```

### Expected Steps
1. Search PubMed
2. Search arXiv
3. Web search
4. Tavily deep search

## Usage Example

> **Note:** Replace `<YOUR_SCP_HUB_API_KEY>` with your own SCP Hub API Key. You can obtain one from the [SCP Platform](https://scphub.intern-ai.org.cn).

```python
import asyncio
import json
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.sse import sse_client

SERVERS = {
    "search-server": "https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search",
    "server-1": "https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory"
}

async def connect(url, transport_type):
    transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "<YOUR_SCP_HUB_API_KEY>"})
    read, write, _ = await transport.__aenter__()
    ctx = ClientSession(read, write)
    session = await ctx.__aenter__()
    await session.initialize()
    return session, ctx, transport

def parse(result):
    try:
        if hasattr(result, 'content') and result.content:
            c = result.content[0]
            if hasattr(c, 'text'):
                try: return json.loads(c.text)
                except: return c.text
        return str(result)
    except: return str(result)

async def main():
    # Connect to required servers
    sessions = {}
    sessions["search-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search", "streamable-http")
    sessions["server-1"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", "sse")

    # Execute workflow steps
    # Step 1: Search PubMed
    result_1 = await sessions["search-server"].call_tool("pubmed_search", arguments={})
    data_1 = parse(result_1)
    print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

    # Step 2: Search arXiv
    result_2 = await sessions["server-1"].call_tool("search_literature", arguments={})
    data_2 = parse(result_2)
    print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

    # Step 3: Web search
    result_3 = await sessions["server-1"].call_tool("search_web", arguments={})
    data_3 = parse(result_3)
    print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

    # Step 4: Tavily deep search
    result_4 = await sessions["search-server"].call_tool("tavily_search", arguments={})
    data_4 = parse(result_4)
    print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")

    # Cleanup
    print("Workflow complete!")

if __name__ == "__main__":
    asyncio.run(main())
```


---
Source attribution: InternScience
Original project: https://github.com/InternScience/scp/blob/cea5398564032aea65a78e246d06c30ae945e03f/skills/web_literature_mining/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2024 Anthropic, PBC

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: InternScience
Original project: https://github.com/InternScience/scp/blob/cea5398564032aea65a78e246d06c30ae945e03f/skills/web_literature_mining/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Education & Research
Roles: Data Scientist, Market Research Analyst, Researcher
Specialties: API
Tools: Arxiv, Pubmed, Tavily, Web Search
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 63853f47bc972bcd811c7f7688576f01a80d38314625a6e70d18b07c70cfef30