How to Use the Legal Fees Apportionment Engine MCP in Pydantic AI

Q: How do I integrate the Legal Fees Apportionment Engine with Pydantic AI?

Use the MCPToolset class pointing to the Vinkius HTTP endpoint. Pass the toolset object to the Agent constructor using the toolsets parameter.

Validate legal fee calculations at runtime using the Legal Fees Apportionment Engine and Pydantic AI.

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Connect Legal Fees Apportionment Engine MCP to Pydantic AI

Create your Vinkius account to connect Legal Fees Apportionment Engine 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 Legal Fees Apportionment Engine with Pydantic AI

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Get type-safe calculations using this MCP Server

The `apportion_legal_fees` tool guarantees that every output matches your required schema. When integrated with Pydantic AI, the agent validates the award splits and fee deductions at runtime, preventing malformed data. If the calculation engine returns an unexpected type, Pydantic AI raises a validation error immediately. This prevents your system from writing corrupted financial records to your database.

Enforce strict financial constraints in Pydantic AI

The `apportion_legal_fees` tool divides judicial awards using exact proportional math. Pydantic AI wraps this tool in strict type models, ensuring that inputs like award amounts are positive floats and percentages do not exceed one hundred. This runtime check prevents agents from making common input errors. The agent cannot pass negative values or invalid party configurations to the calculation engine.

Run award calculations across any LLM provider

The `apportion_legal_fees` tool works independently of your chosen LLM. Pydantic AI allows you to switch between OpenAI, Anthropic, or local models without modifying the underlying calculation logic. This flexibility keeps your application future-proof. You can upgrade your reasoning model while keeping the exact same legal billing math engine intact.

Setup guide

Set up Legal Fees Apportionment Engine 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": {
        "legal-fees-apportionment-engine-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Legal Fees Apportionment Engine transactions")
print(result.output)

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Common questions about Legal Fees Apportionment Engine MCP in Pydantic AI

Use the `MCPToolset` class pointing to the Vinkius HTTP endpoint. Pass the toolset object to the `Agent` constructor using the `toolsets` parameter.

The framework checks the tool response against its internal Pydantic models. If the server output violates the schema, the system raises a validation error before the agent can act on it.

Yes. Since Pydantic AI is model-agnostic, you can connect the MCP server to local LLMs like Llama or Mistral while maintaining full type safety.

Yes. The connection uses standard HTTPS with token authentication. Vinkius manages the endpoint security so your agent can safely query the tool.

The server runs inside a zero-trust sandbox. Your legal fee percentages, award amounts, and client splits are processed in memory and deleted immediately after the calculation completes.

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