How to Use the DefiLlama (DeFi Analytics) MCP in AutoGen

Q: How do I configure DefiLlama (DeFi Analytics) for AutoGen?

Install the autogen-extmcp package. Fetch the tools using mcpservertools with your Vinkius HTTP endpoint, then pass that list directly to your AssistantAgent constructor.

Fuel AutoGen agent debates with hard DeFi data. Let your agents negotiate market strategies using live TVL, APY, and price metrics.

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Connect DefiLlama (DeFi Analytics) MCP to AutoGen

Create your Vinkius account to connect DefiLlama (DeFi Analytics) 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.

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Setup DefiLlama (DeFi Analytics) with AutoGen

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DefiLlama (DeFi Analytics) MCP Server Consensus

This server equips your AutoGen chat rooms with live blockchain financial data. A risk agent can pull `get_pool_historical_data` to argue against a yield strategy proposed by a profit-focused agent. Debates require facts to reach a conclusion. When multiple agents argue over stablecoin stability, one can execute `get_stablecoin_market_cap_chain` to prove a sudden drop in circulating supply.

Multi-Agent Protocol Analysis

Your autonomous teams analyze entire decentralized ecosystems by dividing the research workload. One agent might map the network using `get_protocols` while another fetches specific valuations via `get_current_tvl_chain`. They share their findings in the conversation thread. If the data conflicts with their initial assumptions, the group can collectively decide to run `get_historical_tvl_chain` for deeper historical context.

Cross-Examine Market Volume

The toolkit provides exact exchange volume numbers for agents to challenge each other's market predictions. A skeptical agent will demand proof of liquidity by calling `get_dex_volume_chain` before agreeing to a trade simulation. Nothing gets approved without verification. The system forces agents to justify their market outlook by referencing exact figures from `get_bridge_volume_chain` during their negotiation.

Setup guide

Set up DefiLlama (DeFi Analytics) 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 includes mcp_server_tools for 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 AssistantAgent and call agent.run(). The agent invokes DefiLlama (DeFi Analytics) tools and returns structured results.

agent.py

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="DefiLlama (DeFi Analytics)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent DefiLlama (DeFi Analytics) data")
print(result.messages[-1].content)

Get your connection token →

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. McpWorkbench is 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=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

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="DefiLlama (DeFi Analytics)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent DefiLlama (DeFi Analytics) data")
    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 DefiLlama. 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 DefiLlama (DeFi Analytics) MCP in AutoGen

Install the autogen-ext[mcp] package. Fetch the tools using mcp_server_tools with your Vinkius HTTP endpoint, then pass that list directly to your AssistantAgent constructor.

Yes. You assign the tool list to specific agents based on their role. A dedicated data-fetcher agent can run `get_all_bridges` while the analyst agent waits for the results in the chat.

The McpToolAdapter handles all schema conversions automatically. When an agent calls `get_price_chart`, it receives the data in a format it can easily read and discuss with the group.

They make those decisions independently. The LLM dictates the arguments passed to the MCP tools, meaning an agent might decide to compare Ethereum and Solana by calling `get_current_tvl_chain` for both networks during a debate.

Your internal debates remain completely private. The Vinkius infrastructure operates on ephemeral sandboxes that only process outbound requests for public data like token prices and bridge volumes.

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