How to Use the AntChain MCP in Pydantic AI

Strictly type-safe AntChain interactions for your Pydantic AI agent architecture.

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Connect AntChain MCP to Pydantic AI

Create your Vinkius account to connect AntChain 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 AntChain with Pydantic AI

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Claude

Perplexity

Type-safe blockchain operations

Use `deploy_contract` to push bytecode while ensuring your inputs match expected formats. If the API returns junk, the framework throws an error immediately. Invoke methods via `invoke_contract` with full schema validation. This prevents your agent from passing malformed arguments to the chain.

Precise account state validation

Query account info using `query_account` and let Pydantic models validate every field returned. You get a clean, reliable object instead of raw, unpredictable JSON. Check balances with `query_account_balance` to ensure your agent has enough gas. The framework validates the balance data against your strict type definitions.

Verified ledger inspection

Verify transaction outcomes using `query_transaction` to ensure your agent acts on confirmed data. The runtime validation acts as a final check before the next step. Monitor block details through `query_block` and parse the results into structured Python objects. Your agent logic relies on validated data, not guesses.

Setup guide

Set up AntChain 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": {
        "antchain-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent AntChain transactions")
print(result.output)

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Common questions about AntChain MCP in Pydantic AI

Install the slim package and use the MCPToolset. It handles the connection and validates every response against your models.

Yes, the MCPToolset automatically validates every response. Any mismatch triggers an immediate error before the agent continues.

Provide the HTTP URL to the MCPToolset and register it in your agent. The framework handles the rest of the communication.

Your endpoint is protected by a single token. We manage the handshake so your agent connects securely every time.

We use a V8 isolate sandbox for every request. Your transaction metadata and contract details exist only for the duration of the Pydantic AI agent call.

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