How to Use the Apperio MCP in Pydantic AI

Use Apperio with Pydantic AI for type-safe legal invoice processing where every response is validated at runtime.

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Connect Apperio MCP to Pydantic AI

Create your Vinkius account to connect Apperio 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 Apperio with Pydantic AI

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Type-safe invoice approvals

The `approve_invoice` tool returns data that Pydantic AI checks against your schema immediately. If the response format shifts, your agent catches it before moving forward. This prevents bad data from corrupting your accounting logs. You get a reliable way to manage spend without guessing the data structure.

Validate matter tracking with Pydantic AI

Use `list_matter_tags` to fetch labels and verify them against your internal types. It ensures your agent only applies valid tags when calling `tag_matter`. Your system stays consistent because the types are enforced at the boundary. No more broken logic caused by unexpected API responses.

Monitor legal spend workflows

Get clear, validated paths using `get_invoice_approval_workflow`. Pydantic AI ensures the returned path matches your expected object structure. You build robust systems that handle errors gracefully. If the server returns something weird, your agent crashes safely instead of making a mistake.

Setup guide

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

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

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

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Common questions about Apperio MCP in Pydantic AI

Install the pydantic-ai-slim package and use the MCPToolset class. Point it at your server URL to let the framework handle the type validation for you.

Yes. Whether you use OpenAI, Anthropic, or local models, the type validation happens in your code. This gives you total control over the agent's logic.

The agent will throw a validation error immediately. This stops the execution and prevents your system from processing invalid invoice information.

The MCP server supports both Streamable HTTP and SSE transports. You can configure your toolset to use whichever transport matches your production requirements.

All communication is encrypted in transit and requires a valid endpoint token. We keep your billing details separate from any external model training, protecting your firm's sensitive financial data.

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