KEGG Gene Search

SkillSearch

Search KEGG database for gene information to retrieve pathway associations, functional annotations, and disease links.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the KEGG Gene Search skill

What this skill tells your AI

The instructions your AI receives, as published by internscience/scp in skills/kegg-gene-search/SKILL.md and read by ahel’s review.

Usage

1. MCP Server Definition

import asyncio
import json
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.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:
            print(f"✗ connect failure: {e}")
            return False

    async def disconnect(self):
        try:
            if self.session:
                await self.session_ctx.__aexit__(None, None, None)
            if hasattr(self, 'transport'):
                await self.transport.__aexit__(None, None, None)
        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:

## 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 research

Signals

GitHub stars
167
Forks
9
Last commit
Jun 2026
Advanced
Catalog kind
skill
Gateway key
kegg-gene-search
Source
github.com/internscience/scp