How to Use the Aurorascan (Aurora Network L2 Block Explorer API) MCP in LangChain

Build autonomous Aurora L2 agents with composable tools in LangChain. Chain commands to investigate contracts, track assets, and execute complex on-chain logic.

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Connect Aurorascan (Aurora Network L2 Block Explorer API) MCP to LangChain

Create your Vinkius account to connect Aurorascan (Aurora Network L2 Block Explorer API) to LangChain 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 Aurorascan (Aurora Network L2 Block Explorer API) with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Aurora Data Points

Build agents that reason through multi-step problems on the Aurora network. An agent can start by calling `get_block_no_by_time` to find a specific block, then feed that number into `get_mined_blocks` or `get_tx_list` to investigate what happened at that moment. This isn't just data fetching; it's about connecting the dots. Your agent decides which tool to use next based on the last result. With LangSmith, you can trace the entire chain of thought, seeing exactly how your agent used tools like `get_tx_list_internal` and `proxy_get_transaction_receipt` to arrive at a conclusion.

Debug Smart Contracts with an MCP Server

Give your LangChain agent the tools to inspect and verify smart contracts. It can pull a contract's ABI with `get_abi` and its source with `get_source_code`. Then, it can formulate a read-only call and execute it using `proxy_call` to check the current state. This setup is perfect for building automated contract auditors. Your agent can run a checklist: `verify_source_code` to confirm it's verified, `get_status` to check for failed transactions, and `get_token_supply` to validate tokenomics. It's a verification pipeline that runs on its own.

Build Custom Asset Trackers

Create agents that monitor specific wallets or tokens on Aurora. An agent can periodically use `get_balance_multi` to watch a set of addresses, or `get_token_balance` to track a specific ERC-20 token. It's a simple way to build a custom watchlist. You can chain these actions for more complex jobs. For instance, after finding a new transaction with `get_token_tx`, the agent can immediately get the current ETH/USD price with `get_eth_price` to value the transfer. Because it's LangChain, you can pipe that dollar value directly into a notification or a spreadsheet.

Setup guide

Set up Aurorascan (Aurora Network L2 Block Explorer API) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Aurorascan (Aurora Network L2 Block Explorer API) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "aurorascan-aurora-network-l2-block-explorer-api-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Aurorascan (Aurora Network L2 Block Explorer API) transactions"
    })
    print(result["messages"][-1].content)

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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 Aurorascan. 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 Aurorascan (Aurora Network L2 Block Explorer API) MCP in LangChain

You give the Aurorascan tools to your LangChain agent. The agent can then decide which tool to call—like `get_balance` or `get_tx_list`—to answer a question or complete a task you've given it.

Yes. You can build chains where one step queries the Aurora network using this MCP Server, and the next step sends that data to a database, a different API, or even another LLM for analysis.

A great starter project is a wallet-summary agent. Give it an address, and have it use `get_balance`, `get_tx_list`, and `get_token_balance` to generate a plain-English report of that wallet's holdings and recent activity.

If you're using LangSmith, every call your agent makes to the Aurorascan MCP Server is traced. You'll see the exact inputs and outputs for each tool, which makes debugging your agent's reasoning much easier.

Yes. The server handles public blockchain data like transaction hashes and wallet addresses. Vinkius isolates every user session in an ephemeral V8 sandbox with a zero-trust security model. Your API token is the only thing needed to connect.

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