How to Use the Moneybird MCP in Pydantic AI

Q: How do I install and register the Moneybird MCP Server with Pydantic AI?

Install pydantic-ai-slimmcp and define your server using MCPToolset("http://...") pointing to Vinkius. Pass this toolset into the Agent constructor to give your model validated access to all twelve tools.

Q: How do I verify that the API connection is active?

Run checkapihealth at the start of your agent's execution block to verify the Vinkius bridge. This ensures your script fails gracefully before attempting to write invoices or query records.

Build type-safe bookkeeping agents using Pydantic AI to validate every Moneybird invoice and transaction at runtime.

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Connect Moneybird MCP to Pydantic AI

Create your Vinkius account to connect Moneybird 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 Moneybird with Pydantic AI

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Type-Safe Moneybird MCP Server Invoicing

The `create_sales_invoice` tool builds new drafts while Pydantic AI enforces strict type definitions on the line items and contact fields. This MCP Server integration prevents your agent from sending malformed payloads to the accounting API. If the agent attempts to write an invalid tax rate, the framework raises a runtime validation error immediately. This structure guarantees that every invoice created matches your local Dutch tax schema before it ever hits your ledger.

Validated Bank Mutation Reconciliation

The `list_financial_mutations` tool outputs raw transaction logs that Pydantic AI parses into strongly-typed Python models. Your agent uses these validated records to match bank movements against outstanding invoices. By calling `list_ledger_accounts`, the agent maps each mutation to a verified ledger category. Because every step is validated, you avoid silent data corruption in your financial reports.

Automated Recurring Invoice Checks

The `list_recurring_invoices` tool retrieves your active templates to verify upcoming billing cycles. The agent parses these templates through Pydantic AI schemas to ensure billing dates and amounts are correct. The agent then cross-checks these records with `list_purchase_invoices` to balance projected income against upcoming expenses. This keeps your cash flow forecasts accurate and completely type-safe.

Setup guide

Set up Moneybird 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": {
        "moneybird-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Moneybird MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]` and define your server using `MCPToolset("http://...")` pointing to Vinkius. Pass this toolset into the `Agent` constructor to give your model validated access to all twelve tools.

Pydantic AI will catch the schema mismatch and raise a validation error instead of letting the agent process corrupted data. This is crucial for accounting workflows where a missing field could break tax calculations.

Yes, Pydantic AI is model-agnostic, allowing you to use local models or commercial APIs to run tools like `list_ledger_accounts`. The framework handles the schema validation regardless of which model is executing the tool.

Run `check_api_health` at the start of your agent's execution block to verify the Vinkius bridge. This ensures your script fails gracefully before attempting to write invoices or query records.

Data validation happens locally within your Python runtime, and the Vinkius sandbox handles the API transport securely. Your Dutch tax rates and invoice details are never exposed to third-party logging, maintaining absolute data privacy.

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