How to Use the Nansen (Blockchain Analytics) MCP in LangChain

Build automated blockchain research pipelines for LangChain agents using Nansen (Blockchain Analytics) data.

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Connect Nansen (Blockchain Analytics) MCP to LangChain

Create your Vinkius account to connect Nansen (Blockchain Analytics) 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 Nansen (Blockchain Analytics) with LangChain

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Chain together on-chain intelligence

Feed live blockchain signals directly into your LangChain sequences. You can trigger a `smart_money_netflow` check as the first step in a chain and pass that output into `tgm_who_bought_sold` to identify immediate buyer trends. This setup allows your agent to construct complex, multi-step research reports without human intervention. By chaining these tools, you turn raw transaction data into a structured decision-making loop.

Trace every wallet move with LangSmith

Monitor exactly how your LangChain agent processes Nansen (Blockchain Analytics) data through LangSmith. Every call to `profiler_transactions` or `profiler_related_wallets` generates granular logs, making it simple to debug your reasoning logic. Seeing the agent's thought process in real-time prevents silent failures during data retrieval. You gain total visibility into how the agent filters through large arrays of wallet activity.

Aggregate multi-server data sources

Combine this MCP server with your other LangChain tools to create a unified data layer. Your agent can pull market metrics from `tgm_token_information` while simultaneously querying your private SQL databases for internal context. This integration approach removes the need for custom API wrappers. You just define the tool list and let the LangGraph executor handle the orchestration between different data sources.

Setup guide

Set up Nansen (Blockchain Analytics) 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 Nansen (Blockchain Analytics) 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({
    "nansen-blockchain-analytics-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 Nansen (Blockchain Analytics) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Nansen (Blockchain Analytics) MCP in LangChain

Install the MCP adapters and point your client to the Vinkius endpoint. You initialize the server as a tool source, then pass the output of `get_tools()` into your agent constructor to start executing queries.

Agents can definitely consume the data for signal generation. You use tools like `smart_money_dex_trades` to identify potential opportunities, but you must implement your own execution logic for the actual trade placement.

The server is stateless, but you can maintain context using LangChain's memory components. By using `client.session()`, you keep your research history alive across multiple turns in your conversation.

You should implement a local cache or a rate-limiting layer within your LangGraph workflow. Avoid polling high-frequency endpoints like `tgm_token_ohlcv` too aggressively to ensure your agent stays within the allowed limits.

Your queries stay within the secure Vinkius sandbox. We only handle the specific wallet addresses and token symbols you pass through the tools, ensuring your research strategy remains confidential and isolated.

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