disease_drug_landscape
Disease-Drug Landscape Analysis - Map the drug landscape for a disease: OpenTargets disease drugs, FDA indications, and clinical studies. Use this skill for drug discovery tasks involving get associated drugs by target name get drug names by indication get clinical studies info by drug name. Combines 3 tools from 2 SCP server(s).
Best use case
disease_drug_landscape is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Disease-Drug Landscape Analysis - Map the drug landscape for a disease: OpenTargets disease drugs, FDA indications, and clinical studies. Use this skill for drug discovery tasks involving get associated drugs by target name get drug names by indication get clinical studies info by drug name. Combines 3 tools from 2 SCP server(s).
Teams using disease_drug_landscape 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/disease_drug_landscape/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How disease_drug_landscape Compares
| Feature / Agent | disease_drug_landscape | 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?
Disease-Drug Landscape Analysis - Map the drug landscape for a disease: OpenTargets disease drugs, FDA indications, and clinical studies. Use this skill for drug discovery tasks involving get associated drugs by target name get drug names by indication get clinical studies info by drug name. Combines 3 tools from 2 SCP server(s).
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
# Disease-Drug Landscape Analysis
**Discipline**: Drug Discovery | **Tools Used**: 3 | **Servers**: 2
## Description
Map the drug landscape for a disease: OpenTargets disease drugs, FDA indications, and clinical studies.
## Tools Used
- **`get_associated_drugs_by_target_name`** from `opentargets-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets`
- **`get_drug_names_by_indication`** from `fda-drug-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug`
- **`get_clinical_studies_info_by_drug_name`** from `fda-drug-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug`
## Workflow
1. Get associated drugs from OpenTargets
2. Find drugs by indication in FDA
3. Get clinical studies for top drug
## Test Case
### Input
```json
{
"target_name": "EGFR",
"indication": "non-small cell lung cancer"
}
```
### Expected Steps
1. Get associated drugs from OpenTargets
2. Find drugs by indication in FDA
3. Get clinical studies for top drug
## 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 = {
"opentargets-server": "https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets",
"fda-drug-server": "https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug"
}
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["opentargets-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets", "streamable-http")
sessions["fda-drug-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", "streamable-http")
# Execute workflow steps
# Step 1: Get associated drugs from OpenTargets
result_1 = await sessions["opentargets-server"].call_tool("get_associated_drugs_by_target_name", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Find drugs by indication in FDA
result_2 = await sessions["fda-drug-server"].call_tool("get_drug_names_by_indication", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Get clinical studies for top drug
result_3 = await sessions["fda-drug-server"].call_tool("get_clinical_studies_info_by_drug_name", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Cleanup
print("Workflow complete!")
if __name__ == "__main__":
asyncio.run(main())
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