How to Use the Lineascan MCP in LangChain

Q: Is Lineascan data persistent in LangChain sessions?

The client is stateless by default. You must use client.session() if you need to keep context across multiple agent turns.

Build multi-step blockchain reasoning chains for Linea using Lineascan and LangChain agents.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Lineascan MCP to LangChain

Create your Vinkius account to connect Lineascan 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.

GDPR Free for Subscribers

Setup Lineascan with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain blockchain data in LangChain

Connect Lineascan tools directly into your LangChain sequences. You define the logic where the output of `tx_list` feeds into `get_abi` for automated contract analysis. Your agent handles the flow between these nodes. It determines the sequence of calls needed to audit a contract without you writing manual bridge code.

Trace your Lineascan MCP Server calls

Every tool execution is captured by LangSmith. You see exactly what your agent passed to `eth_call` or `get_logs` and how long the network took to respond. This visibility prevents black-box failures during chain execution. You debug the reasoning process by inspecting the exact input and output of every single MCP tool call.

Aggregate Lineascan with other sources

LangChain lets you combine Lineascan with your existing databases or vector stores. Your agent compares real-time values from `eth_price` against your historical stored data. This cross-referencing happens in a single execution loop. You build pipelines that verify on-chain data against your internal records.

Setup guide

Set up Lineascan 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 Lineascan 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({
    "lineascan-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 Lineascan transactions"
    })
    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 Lineascan. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Lineascan now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Lineascan MCP in LangChain

Install the MCP adapters and point your client to the Vinkius endpoint. Use `client.get_tools()` to expose the 25 endpoints to your agent.

You manage rate limits within your agent's execution policy. LangChain allows you to insert pauses or retries between MCP tool calls to stay within API bounds.

The client is stateless by default. You must use `client.session()` if you need to keep context across multiple agent turns.

Yes. The underlying implementation handles async requests, allowing your chains to fire multiple queries in parallel without blocking.

Your API key and address queries stay between your local agent and the Vinkius sandbox. No data is stored on our servers after the request clears.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python