How to Use the KEGG MCP in AutoGen

Enable multi-agent deliberation for genomic tasks using AutoGen.

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Connect KEGG MCP to AutoGen

Create your Vinkius account to connect KEGG to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup KEGG with AutoGen

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Perplexity

Debate genomic findings in AutoGen

Set up a research agent to query `kegg_get` while a reviewer agent checks the output. They negotiate the final interpretation of the pathway data. This setup catches errors before the final response is generated. You get a consensus-driven answer that's been vetted by multiple perspectives.

Negotiate drug interaction risks

Task your security agent with running `kegg_ddi` queries to flag risks. If a conflict appears, the agents discuss the severity before alerting you. You avoid alert fatigue by letting the agents handle the initial filtering. They only escalate when they agree on a potential problem.

Automate cross-referencing between agents

Use `kegg_conv` to translate identifiers when agents disagree on terminology. One agent can request a conversion, and the other can verify the result. This keeps your agents on the same page. It's a clean way to manage complex data mapping across a multi-agent system.

Setup guide

Set up KEGG MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes KEGG tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="KEGG_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent KEGG data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="KEGG_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent KEGG data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by KEGG. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about KEGG MCP in AutoGen

You register the server tools with the AssistantAgent. All agents in the conversation group can then call the tools as needed.

Absolutely. That's the point of the framework. Agents can challenge each other's interpretations of pathway results until they converge.

You define a lead agent to manage tool calls. This prevents redundant queries and keeps API traffic within reasonable bounds.

The McpToolAdapter automatically maps the server schema to the agent's expected format. No manual configuration is required.

Yes. The agent conversation state remains local to your environment. No patient-specific genomic data is shared outside your control.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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