How to Use the Wave Financial MCP in Pydantic AI

Q: How do I list customer details for Wave Financial using Pydantic AI?

You call listcustomers. Because of Pydantic validation, you'll get a clean, typed Python object containing all the necessary client information without having to worry about formatting errors.

Q: Does Pydantic AI handle transaction data for Wave Financial?

Yes. The listtransactions tool provides access to accounting transactions. These responses are validated, so you can trust the structure of every single record.

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Connect Wave Financial MCP to Pydantic AI

Create your Vinkius account to connect Wave Financial 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 Wave Financial with Pydantic AI

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Type-Safe Invoice Retrieval

The agent pulls detailed invoice records by calling `list_invoices`. The critical benefit here is that every field returned—invoice number, date, amount—is validated against a Pydantic model. If the Wave Financial API sends back data in an unexpected format, your agent doesn't fail silently; it throws a clear validation error.

Reliable Product and Tax Data Access

You can list available products via `list_products` or check configured sales taxes with `list_sales_taxes`. With Pydantic AI, you know exactly what data structure you're working with. This model-agnostic approach means correctness matters more than speed, and the framework guarantees that correctness.

Guaranteed Data Integrity from MCP Server

The Pydantic AI framework requires every MCP response to pass rigorous type checks. If the API returns garbage data or a missing field, your agent stops immediately with an error. This prevents silent corruption—the worst thing that can happen in automated financial reporting.

Setup guide

Set up Wave Financial 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": {
        "wave-financial-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Wave Financial MCP in Pydantic AI

You call `list_customers`. Because of Pydantic validation, you'll get a clean, typed Python object containing all the necessary client information without having to worry about formatting errors.

Yes. The `list_transactions` tool provides access to accounting transactions. These responses are validated, so you can trust the structure of every single record.

The main benefit is reliability. It validates all data returned from the server against strict models, eliminating unexpected or malformed fields that could break a production agent.

The agent fails loudly. It will raise an explicit validation error showing exactly which field was wrong or missing, letting you fix the source issue immediately.

The server touches various core financial data types. For example, when calling `list_bills`, it provides validated accounts payable records.

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