kegg-gene-search
Search KEGG database for gene information to retrieve pathway associations, functional annotations, and disease links.
Best use case
kegg-gene-search is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Search KEGG database for gene information to retrieve pathway associations, functional annotations, and disease links.
Teams using kegg-gene-search 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/kegg-gene-search/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How kegg-gene-search Compares
| Feature / Agent | kegg-gene-search | 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 KEGG database for gene information to retrieve pathway associations, functional annotations, and disease links.
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
# KEGG Gene Search
## Usage
### 1. MCP Server Definition
```python
import asyncio
import json
from contextlib import AsyncExitStack
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
class OrigeneClient:
"""Origene-KEGG 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):
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)
```
### 2. Gene Search Workflow
**Implementation:**
```python
## Initialize client
client = OrigeneClient(
"https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG",
"<your-api-key>"
)
if not await client.connect():
print("connection failed")
exit()
## Search KEGG genes database
result = await client.session.call_tool(
"kegg_find",
arguments={
"db": "genes",
"query": "p53",
"option": ""
}
)
result_data = client.parse_result(result)
print(result_data)
await client.disconnect()
```
### Tool Descriptions
**Origene-KEGG Server:**
- `kegg_find`: Search KEGG database
- Args:
- `db` (str): Database to search (e.g., "genes", "pathway")
- `query` (str): Search query
- `option` (str): Additional options
- Returns: KEGG gene entries with pathway and functional information
### Use Cases
- Pathway analysis
- Gene functional annotation
- Disease gene identification
- Systems biology researchRelated Skills
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