How to Use the Holded MCP in Pydantic AI

Q: Why should I use Pydantic AI instead of raw API calls for Holded?

It stops LLM hallucinations. If your agent tries to read listcontacts but receives unexpected data structures, the framework rejects the payload instead of passing bad data to your code.

Build type-safe billing pipelines using Pydantic AI and this verified Holded MCP Server.

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Connect Holded MCP to Pydantic AI

Create your Vinkius account to connect Holded 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 Holded with Pydantic AI

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Guarantee runtime safety with Pydantic AI

The `create_invoice` tool requires strict parameter validation before execution. By using Pydantic AI, your agent validates contact IDs and item schemas locally before sending them to Holded. If the model attempts to generate an invoice with missing fields, the framework catches the error before making the API call. This prevents bad data from corrupting your Spanish tax reporting.

Validate inventory and quotes without silent errors

The `list_products` tool returns structured inventory data that can vary. Pydantic AI enforces strict type-checking on every product field, ensuring your agent never processes a null price or missing SKU. You get the same type-safety when querying sales pipelines. Tools like `list_quotes` and `list_sales_orders` map directly to Python models, giving you autocompletion and compile-time confidence.

Monitor localized business expenses safely

The `list_expenses` tool exposes tax-deductible outlays that require precise categorization. This MCP Server lets your agent inspect financial transactions while ensuring every record matches your internal Python schemas. You can safely feed these validated records into downstream accounting tools. If a purchase invoice from `list_purchase_invoices` lacks a tax ID, the system fails loudly so you can fix it immediately.

Setup guide

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

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

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

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Common questions about Holded MCP in Pydantic AI

Use the unified MCPToolset constructor with your Vinkius HTTP endpoint. Pass the toolset directly to your Agent instance to start querying your ERP data.

It stops LLM hallucinations. If your agent tries to read `list_contacts` but receives unexpected data structures, the framework rejects the payload instead of passing bad data to your code.

Yes. The server handles concurrent requests efficiently. Your agent can query `list_invoices` and `list_payments` in parallel without blocking your main application loop.

Yes. Pydantic AI is model-agnostic. You can run this MCP Server with Ollama, Gemini, or Anthropic models while maintaining strict runtime validation.

Your raw Spanish tax invoices and expense records are processed in secure, short-lived V8 environments. Vinkius handles the credentials securely, ensuring your API tokens never leak into model logs or external storage.

Use it with your favorite AI tools

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