How to Use the Dune Analytics (Web3 SQL Analytics API) MCP in AutoGen
Let your AutoGen agents debate, execute, and verify complex blockchain SQL queries in AutoGen multi-agent workflows.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Dune Analytics (Web3 SQL Analytics API) MCP to AutoGen
Create your Vinkius account to connect Dune Analytics (Web3 SQL Analytics API) to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.
Drive multi-agent consensus with this MCP Server
The `execute_query` tool lets your data-analyst agent pull live blockchain tables while other agents wait for the results. One agent writes the SQL, triggers the execution, and shares the execution ID with the group. A separate verification agent monitors the query using `get_execution_status`. Once completed, it fetches the rows using `get_execution_results` and passes them to a critic agent to verify the financial logic.
Manage query budgets via collaborative cancellation
A budget-monitoring agent can invoke `cancel_execution` if a query runs too long or exceeds credit limits. This MCP Server setup prevents autonomous loops from burning through your Dune API credits on accidental infinite queries. The decision to cancel is handled entirely through agent conversation. If the status checking tool shows a query is stuck, the manager agent commands the executor to stop the run immediately.
Verify data state before agent decisions
The `get_execution_status` tool ensures your agents never argue over incomplete data. The analyst agent must confirm a successful run status before presenting the final dataset to the rest of the group. This protocol prevents agents from making decisions based on empty tables or partial runs. It creates a reliable checkpoint in your multi-step autonomous workflows.
Set up Dune Analytics (Web3 SQL Analytics API) MCP in AutoGen
Prerequisites
- Python 3.10+ installed
-
autogen-ext[mcp]package - Active Vinkius subscription with a valid endpoint token
- 1
Install AutoGen with MCP
Run
pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includesmcp_server_toolsfor stateless tool access. - 2
Fetch tools from the MCP
Call
mcp_server_tools(SseServerParams(url=...))with your Vinkius endpoint. Replace[YOUR_TOKEN_HERE]with your token from cloud.vinkius.com. - 3
Run your agent
Pass the tools to
AssistantAgentand callagent.run(). The agent invokes Dune Analytics (Web3 SQL Analytics API) tools and returns structured results.
from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
server_params = SseServerParams(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
tools = await mcp_server_tools(server_params)
agent = AssistantAgent(
name="Dune Analytics (Web3 SQL Analytics API)_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
tools=tools,
)
result = await agent.run("List recent Dune Analytics (Web3 SQL Analytics API) data")
print(result.messages[-1].content) Prerequisites
- Python 3.10+ installed
-
autogen-ext[mcp]+autogen-agentchat - Active Vinkius subscription with a valid endpoint token
- 1
Install dependencies
Same packages as above.
McpWorkbenchis ideal when your agent needs stateful sessions across multiple tool calls. - 2
Use McpWorkbench as context manager
Wrap your agent in
async with McpWorkbench(...)to maintain shared state and resources. The workbench manages the full MCP session lifecycle. - 3
Run with workbench
Pass
workbench=workbenchto your agent. State is preserved across multiple tool calls within the same session.
from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
server_params = SseServerParams(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
async with McpWorkbench(server_params) as workbench:
agent = AssistantAgent(
name="Dune Analytics (Web3 SQL Analytics API)_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
workbench=workbench,
)
result = await agent.run("List recent Dune Analytics (Web3 SQL Analytics API) data")
print(result.messages[-1].content) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Dune Analytics. 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 Dune Analytics (Web3 SQL Analytics API) MCP in AutoGen
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