drugsda-target-retrieve
Search the protein information from the input gene name and downloads the optimal PDB or AlphaFold structures.
Best use case
drugsda-target-retrieve is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Search the protein information from the input gene name and downloads the optimal PDB or AlphaFold structures.
Teams using drugsda-target-retrieve should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/drugsda-target-retrieve/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How drugsda-target-retrieve Compares
| Feature / Agent | drugsda-target-retrieve | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Search the protein information from the input gene name and downloads the optimal PDB or AlphaFold structures.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Target Protein Retrieve
## Usage
### 1. MCP Server Definition
```python
import json
from contextlib import AsyncExitStack
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
class DrugSDAClient:
def __init__(self, server_url: str):
self.server_url = server_url
self.session = None
async def connect(self):
print(f"server url: {self.server_url}")
try:
self.transport = streamablehttp_client(
url=self.server_url,
headers={"SCP-HUB-API-KEY": "sk-a0033dde-b3cd-413b-adbe-980bc78d6126"}
)
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()
session_id = self.get_session_id()
print(f"✓ connect success")
return True
except Exception as e:
print(f"✗ connect failure: {e}")
import traceback
traceback.print_exc()
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. Retrieve Protein Structure
The description of tool *retrieve_protein_structure_by_gene_name*.
```tex
Search the protein information from the input gene name and downloads the optimal PDB or AlphaFold structures. Note that species support is limited to humans only.
Args:
gene_name (str): Input gene name (e.g., 'TP53')
Return:
status (str): success/error
msg (str): message
prot_structure_path (str): Path to the downloaded protein structure file (pdb format)
```
How to use tool *retrieve_protein_structure_by_gene_name* :
```python
client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool")
if not await client.connect():
print("connection failed")
return
response = await client.session.call_tool(
"retrieve_protein_structure_by_gene_name",
arguments={
"gene_name": gene_name
}
)
result = client.parse_result(response)
prot_structure_path = result["prot_structure_path"]
await client.disconnect()
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