How to Use the KEGG MCP in LlamaIndex

Index live genomic and chemical data into RAG pipelines using LlamaIndex.

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Connect KEGG MCP to LlamaIndex

Create your Vinkius account to connect KEGG to LlamaIndex 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 LlamaIndex

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Index KEGG content with LlamaIndex

Convert `kegg_info` and `kegg_get` responses into searchable nodes. LlamaIndex stores this data so your agent can query it semantically later. This turns static database lookups into a persistent knowledge base. You're building a system that learns from the API data over time.

Ground AI answers in live data

Force your agent to retrieve facts using `kegg_find` before answering a prompt. It prevents hallucinations by grounding every claim in current database entries. Your index stays relevant because the agent pulls the latest data during the retrieval phase. It's direct access to the source.

Searchable pathways for RAG apps

Use `kegg_list` to populate your vector store with organism-specific pathways. LlamaIndex then allows for efficient retrieval during complex user queries. You avoid redundant API calls by querying your local index first. It's faster and respects the source database usage limits.

Setup guide

Set up KEGG MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all KEGG MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to KEGG tools.",
)
response = await agent.run("List recent KEGG data")

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Common questions about KEGG MCP in LlamaIndex

You wrap the tools in an McpToolSpec and pass the output to your ingest pipeline. The agent then converts the raw data into indexed nodes.

Yes. You can limit the available tools to specific functions like `kegg_get` or `kegg_find` within your agent configuration.

Once you index the results, you can perform natural language searches against the retrieved pathways and gene descriptions.

Data is only held in your local index. We don't store chemical structures or genomic records on our servers.

Trigger a re-indexing run using the standard tools. The agent will fetch the latest database records and update your local vector store.

Use it with your favorite AI tools

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