How to Use the DeBank (DeFi Wallet Tracker) MCP in Pydantic AI

Q: How do I integrate DeBank (DeFi Wallet Tracker) with Pydantic AI?

Install pydantic-ai-slimmcp and initialize an MCPToolset pointing to your Vinkius HTTP URL. Pass this directly into your Agent's toolsets parameter.

Q: Can Pydantic AI validate token approvals from DeBank (DeFi Wallet Tracker)?

The agent triggers getusertokenauthorizedlist to fetch active allowances. Your models then verify the spender addresses and amounts before proceeding.

Q: Does DeBank (DeFi Wallet Tracker) provide chain data to Pydantic AI?

You can call listchains and getchain to pull network IDs, native tokens, and block explorer URLs. The framework ensures these details match your expected types.

Q: How do I track top token holders?

The gettokentopholders tool returns an array of wallet addresses and their respective balances. Your agent parses this list to detect concentration risks.

Build type-safe DeFi agents by connecting Pydantic AI to DeBank for strictly validated wallet and protocol data.

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Connect DeBank (DeFi Wallet Tracker) MCP to Pydantic AI

Create your Vinkius account to connect DeBank (DeFi Wallet Tracker) to Pydantic AI 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 DeBank (DeFi Wallet Tracker) with Pydantic AI

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Type-safe wallet tracking with Pydantic AI

The DeBank MCP Server feeds raw on-chain portfolio data into your type-checked Python application. When your agent calls `get_user_total_balance` or `get_user_protocol_positions`, the framework validates the incoming JSON against your predefined Pydantic models. If the API returns a string instead of a float for a token balance, the agent fails loudly. You set up the connection by initializing an `MCPToolset` with your Vinkius HTTP endpoint. Passing this to your Agent guarantees that every wallet address, chain ID, and balance metric adheres strictly to your schema. No silent data corruption. No hallucinated fields.

Validate transaction simulations

Executing transactions blindly is a fast way to lose funds. This MCP integration provides `pre_exec_tx` and `explain_tx` so your agent can simulate trades before signing. Your Pydantic models enforce that the simulation output contains the exact expected token outflows and gas estimates. The server also tracks API usage via `get_account_units`. Your application monitors its rate limits dynamically, failing gracefully if the agent attempts to pull too many records at once. You dictate the exact data shape, and the framework enforces it at runtime.

Build reliable protocol datasets

Model-agnostic agents need consistent data structures regardless of which LLM is driving the logic. By calling `list_protocols` and `get_pool`, your agent gathers liquidity metrics across multiple chains. Pydantic AI ensures that a Gemini model and an OpenAI model both process the exact same structured output. Vinkius removes the authentication burden from your code. You supply the single endpoint token to the `MCPToolset`, and the MCP Server securely fetches the required data. Your focus stays entirely on writing the validation logic for `get_token_history_price` and `get_gas_market`.

Setup guide

Set up DeBank (DeFi Wallet Tracker) MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "debank-defi-wallet-tracker-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to DeBank (DeFi Wallet Tracker) tools.",
)

result = await agent.run("List recent DeBank (DeFi Wallet Tracker) transactions")
print(result.output)

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 DeBank. 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 DeBank (DeFi Wallet Tracker) MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]` and initialize an `MCPToolset` pointing to your Vinkius HTTP URL. Pass this directly into your Agent's `toolsets` parameter.

The agent triggers `get_user_token_authorized_list` to fetch active allowances. Your models then verify the spender addresses and amounts before proceeding.

You can call `list_chains` and `get_chain` to pull network IDs, native tokens, and block explorer URLs. The framework ensures these details match your expected types.

The `get_token_top_holders` tool returns an array of wallet addresses and their respective balances. Your agent parses this list to detect concentration risks.

Vinkius isolates your session inside an ephemeral V8 sandbox. The server pulls your specific wallet addresses, transaction hashes, and gas metrics, delivers the JSON to your client, and wipes the memory completely when the execution finishes.

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