How to Use the Dune Analytics (Web3 SQL Analytics API) MCP in LangChain

Q: How do I handle long-running queries with Dune Analytics (Web3 SQL Analytics API) in LangChain?

Use executequery to start the run, then set up a polling loop in your chain using getexecutionstatus. Once the status returns complete, pass the ID to getexecutionresults to fetch the data.

Q: Can my LangChain agent stop a query if it runs too long?

Yes. Your agent can call cancelexecution using the ID returned from the initial query. This stops the query immediately on Dune's servers, saving your API credits.

Q: Do I need to write custom parsers for the SQL output?

No. The getexecutionresults tool returns structured JSON data. Your agent can read this raw data directly to make decisions or pass it to another tool in the chain.

Execute live blockchain SQL queries and feed raw Web3 data directly into your LangChain decision loops.

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Connect Dune Analytics (Web3 SQL Analytics API) MCP to LangChain

Create your Vinkius account to connect Dune Analytics (Web3 SQL Analytics 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 Dune Analytics (Web3 SQL Analytics API) with LangChain

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Chain raw SQL execution into LangChain agents

The `execute_query` tool kicks off SQL queries on Dune's database directly from your active chain. Your agent receives a unique execution ID immediately, allowing the pipeline to move to other tasks or poll for status without blocking the entire run. By monitoring the run with `get_execution_status`, your chain decides when to pull the final dataset. Once the status shows complete, the agent calls `get_execution_results` to feed raw blockchain tables straight into the next step of your sequence.

Control run costs with active cancellation

Long-running queries eat up your API credits fast. This MCP server lets your chain inspect execution times and invoke `cancel_execution` if a query takes too long or if a user aborts the request mid-run. Your agent handles this logic dynamically. If a status check returns a pending state for too many cycles, the chain triggers the cancel tool to keep your credit usage under tight control.

Trace Web3 data pipelines with LangSmith

Every call to `execute_query` or `get_execution_results` shows up as a discrete step in your LangSmith dashboard when using this MCP Server. You see the exact SQL parameters sent by the agent and the raw payload returned from the blockchain. This visibility makes debugging easy. When a chain fails, you can isolate whether the issue was a poorly written SQL query or a timeout during the polling phase.

Setup guide

Set up Dune Analytics (Web3 SQL Analytics 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 Dune Analytics (Web3 SQL Analytics 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({
    "dune-analytics-web3-sql-analytics-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 Dune Analytics (Web3 SQL Analytics API) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Dune Analytics (Web3 SQL Analytics API) MCP in LangChain

Use `execute_query` to start the run, then set up a polling loop in your chain using `get_execution_status`. Once the status returns complete, pass the ID to `get_execution_results` to fetch the data.

Yes. Your agent can call `cancel_execution` using the ID returned from the initial query. This stops the query immediately on Dune's servers, saving your API credits.

The server runs in a sandbox on Vinkius, which handles connection pooling. You should design your LangChain agent to check status periodically rather than spamming the API to avoid hitting rate limits.

No. The `get_execution_results` tool returns structured JSON data. Your agent can read this raw data directly to make decisions or pass it to another tool in the chain.

Vinkius hosts the server in an ephemeral, zero-trust V8 sandbox. Your API keys and SQL query parameters are never written to persistent disks, and they remain completely isolated from other users' runtimes.

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