How to Use the AntChain MCP in LlamaIndex

Index your AntChain blockchain data directly into LlamaIndex for grounded, searchable insights.

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Connect AntChain MCP to LlamaIndex

Create your Vinkius account to connect AntChain 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 AntChain with LlamaIndex

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Search AntChain history with LlamaIndex

Turn raw blockchain logs into a searchable knowledge base. Use `query_block` and `query_latest_transactions` to fetch data, then let your index store it for future reference. You stop guessing what happened on the chain. Instead, you query your index to find specific transaction patterns or historical metadata.

Retrieve contract data for LlamaIndex RAG

Pull configuration details using `query_contract` and feed them into your retrieval pipeline. This gives your agent context about how smart contracts are actually structured. Your RAG application becomes context-aware. It understands the state of your deployments because it pulls the data directly from the source.

Sync AntChain balances to your LlamaIndex knowledge base

Keep your agent updated on account health by indexing `query_account_balance` results. You can track changes over time without manual polling. The tool output becomes part of your vector store. You can then ask your agent about account trends and get answers grounded in the real data.

Setup guide

Set up AntChain 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 AntChain 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 AntChain tools.",
)
response = await agent.run("List recent AntChain data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AntChain. 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 AntChain MCP in LlamaIndex

You use the MCP tool spec to fetch data and then pass those results into your document loader. LlamaIndex then converts that data into vector embeddings.

Yes. Once the data is indexed, the chat engine can retrieve relevant information from your blockchain snapshots to answer user questions.

Absolutely. The tool outputs are plain text and metadata, which map perfectly to the document structure LlamaIndex expects.

The MCP client automates the fetching. You simply define the workflow to index the results returned by the tools.

We only process public account balances and block metadata. Your sensitive authentication tokens are handled by the server endpoint, never stored in your local vector index.

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