How to Use the Dynamic (Web3 Auth) MCP in Pydantic AI

Q: How do I hook up Dynamic (Web3 Auth) to a Pydantic AI agent?

Instantiate MCPToolset with your Vinkius HTTP endpoint and pass it to your Agent's toolsets parameter. Pydantic AI automatically handles the schema generation and runtime validation for all Web3 auth tools.

Q: Does Pydantic AI validate the output of Dynamic (Web3 Auth) balance checks?

Yes, when calling gettokenbalances, the returned balance data is validated against strict Pydantic models at runtime. If the API returns malformed balance structures, the agent fails loudly instead of processing bad numbers.

Validate Web3 auth profiles and wallet balances at runtime using Pydantic AI with this type-safe MCP Server.

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Connect Dynamic (Web3 Auth) MCP to Pydantic AI

Create your Vinkius account to connect Dynamic (Web3 Auth) 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 Dynamic (Web3 Auth) with Pydantic AI

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Strictly Typed Web3 Profile Resolution

The `get_user` tool fetches Web3 profile data and parses it directly into type-safe Python objects within your Pydantic AI agent. If the Dynamic API structure changes or returns unexpected fields, your agent raises a validation error immediately. Using the MCP standard, your agent acts only on verified, schema-compliant profiles, eliminating runtime surprises when handling Web3 identities. This strict parsing prevents silent corruption in your production database.

Type-Safe Wallet Compliance Checks

The `check_sanctions` tool validates wallet addresses against global blocklists before your Pydantic AI agent executes any financial logic. The tool's output is validated against strict schemas to guarantee that sanction statuses are never misread. By forcing strict type checking on compliance data, you eliminate the risk of model hallucinations. The agent cannot guess or misinterpret the sanction status because the runtime enforces the correct boolean structure.

Session Control inside Pydantic AI

The `revoke_session` and `delete_user` tools allow your Pydantic AI agent to manage state changes safely using this MCP Server. If a user triggers a deletion request, the agent executes the cleanup and verifies the response matches your defined models. This ensures your data hygiene routines are completely predictable. Any failure in the session termination pipeline triggers an immediate exception, allowing your system to fall back safely.

Setup guide

Set up Dynamic (Web3 Auth) 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": {
        "dynamic-web3-auth-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Dynamic (Web3 Auth) tools.",
)

result = await agent.run("List recent Dynamic (Web3 Auth) transactions")
print(result.output)

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Common questions about Dynamic (Web3 Auth) MCP in Pydantic AI

Instantiate `MCPToolset` with your Vinkius HTTP endpoint and pass it to your Agent's `toolsets` parameter. Pydantic AI automatically handles the schema generation and runtime validation for all Web3 auth tools.

Yes, when calling `get_token_balances`, the returned balance data is validated against strict Pydantic models at runtime. If the API returns malformed balance structures, the agent fails loudly instead of processing bad numbers.

Yes, Pydantic AI is completely model-agnostic, meaning you can run these Web3 auth tools using OpenAI, Anthropic, or local models while maintaining strict runtime type safety.

The runtime validation will capture the unexpected version string immediately. This allows your code to handle legacy or unrecognized wallet versions gracefully without crashing silently.

This MCP Server runs in an ephemeral, zero-trust V8 sandbox on Vinkius, ensuring session tokens and wallet balances are never persisted or exposed. Your Pydantic AI agent processes these variables strictly in memory to prevent data leaks.

Use it with your favorite AI tools

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