How to Use the Lexzur MCP in Pydantic AI

Q: Can my agent query corporate contacts in Lexzur?

Yes, the agent uses listcontacts and listcompanies to fetch client directories. The framework guarantees that the returned contact objects match your defined Python models.

Q: How does Lexzur protect our legal documents when integrated with Pydantic AI?

Your documents and case files accessed via listdocuments are isolated in a secure, ephemeral sandbox. Vinkius manages the single endpoint token, ensuring your legal documents are never exposed to unauthorized external networks.

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Connect Lexzur MCP to Pydantic AI

Create your Vinkius account to connect Lexzur 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 Lexzur with Pydantic AI

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Validate matter creation with Pydantic AI

The `create_matter` tool runs with strict runtime type-checking when invoked through your Pydantic AI agent. The framework validates the inputs against your Pydantic models before hitting the Lexzur API. If an attorney enters a malformed date or missing client ID, the agent fails immediately. This prevents corrupted data from entering your legal database during high-volume intakes.

Track billing errors with this MCP Server

This Lexzur MCP Server exposes `list_invoices` and `list_time_logs` to enforce strict validation on your financial workflows. Your Pydantic AI agent parses invoice balances and billable hours against strict Python schemas. If a time log contains invalid characters or negative hours, the system triggers a validation error. This ensures your automated billing pipeline remains accurate and audit-ready.

Manage legal tasks without silent failures

The `list_tasks` tool lets your Pydantic AI agent retrieve and inspect outstanding legal assignments. The agent validates task schemas at runtime, catching API drift or unexpected null fields instantly. By combining this with `list_matters`, the agent maps tasks to their parent cases with absolute type safety. You get clean, predictable legal operations without worrying about hallucinated fields.

Setup guide

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

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

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

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Common questions about Lexzur MCP in Pydantic AI

Install pydantic-ai-slim with the mcp extra, then instantiate MCPToolset with your Vinkius HTTP URL. Pass this toolset object into your Agent constructor to enable the tools.

Pydantic AI enforces type-safety at runtime, meaning any data returned by `get_matter` is validated against strict Python schemas. This prevents your agent from executing actions on corrupted data.

No, you should use the unified MCPToolset class instead. It supports both Streamable HTTP and SSE transports, providing a more stable connection to your MCP server.

Yes, the agent uses `list_contacts` and `list_companies` to fetch client directories. The framework guarantees that the returned contact objects match your defined Python models.

Your documents and case files accessed via `list_documents` are isolated in a secure, ephemeral sandbox. Vinkius manages the single endpoint token, ensuring your legal documents are never exposed to unauthorized external networks.

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