How to Use the Modulr MCP in Pydantic AI

Q: How do I list payments in Pydantic AI?

Use modulrlistpayments to pull your transaction arrays. Your agent will validate the entire list against your schema before it proceeds with any analysis.

Strict type-safe banking for Pydantic AI. Execute payments with runtime validation for every transaction.

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Connect Modulr MCP to Pydantic AI

Create your Vinkius account to connect Modulr 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 Modulr with Pydantic AI

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Type-safe payment execution in Pydantic AI

Invoke `modulr_create_payment` with full Pydantic model validation. If your agent tries to send an invalid amount or a malformed IBAN, your application catches the error before the request hits the network. This prevents silent failures in your payment flow. You get a clean, validated response every time your agent executes a transaction.

Validate account state using Pydantic AI

Use `modulr_get_accounts` to fetch your current balance and account status. The server maps the raw API response to your Pydantic models, ensuring your agent knows exactly what it can spend. Any unexpected field in the bank's response triggers a validation error. You never hallucinate account data, ensuring your agent's financial decisions are grounded in hard facts.

Manage beneficiaries with Pydantic AI

Register recipients using `modulr_create_beneficiary` while enforcing strict data schemas. Your agent maintains the integrity of your payee list by validating every input against your defined models. Use `modulr_get_customers` to verify your existing entities. You ensure your agent only interacts with authorized accounts, keeping your integration clean and error-free.

Setup guide

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

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

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

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Common questions about Modulr MCP in Pydantic AI

Yes. Every tool response is checked against your Pydantic models at runtime. If the API returns something unexpected, the agent stops immediately.

Install the MCP toolset and pass your server URL to the MCPToolset class. It integrates directly into your agent's toolset list.

It is designed for it. The tools output structured data that maps perfectly to your Python models, giving you total control over the agent's logic.

Use `modulr_list_payments` to pull your transaction arrays. Your agent will validate the entire list against your schema before it proceeds with any analysis.

The server uses HMAC-SHA256 to verify the origin and integrity of every request. Your financial data is protected by cryptographic signatures, and the server does not persist your transaction logs.

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