How to Use the Clio MCP in Pydantic AI

Q: Can I use Pydantic AI to create a new client contact in Clio?

Yes, your agent can call the createcontact tool. Pydantic AI helps ensure you provide the right arguments, like a firstname and lastname for a person, or setting the type to 'Company' for a business.

Build type-safe legal agents with Pydantic AI and the Clio MCP Server. Ensure every action in your practice is validated and correct.

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Connect Clio MCP to Pydantic AI

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Setup Clio with Pydantic AI

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Enforce Data Correctness in Clio

Pydantic AI's main job is to make sure data is correct. When your agent calls `get_contact` or `get_matter`, the response from the Clio MCP Server is automatically validated against a Pydantic model. If a field is missing or has the wrong type, your code raises a `ValidationError` immediately. This prevents silent data corruption. You'll never have an agent that thinks a contact's name is a phone number or a matter ID is a date. This strictness is critical when dealing with sensitive legal data using tools like `create_contact` and `create_matter`.

Model-Agnostic Task Automation with Pydantic AI

Write your agent logic once and run it with any LLM—OpenAI, Anthropic, Gemini, you name it. Your agent can `create_task` for a court deadline or `create_note` to log a client call, and the Pydantic AI framework handles the tool-calling mechanics for you. Because the tool interactions are strictly typed, you can swap the underlying model without rewriting your Clio automation code. The validation layer ensures that no matter how the LLM formats its output, the call to the Clio server is always correct.

Run Reliable Financial Operations

Managing firm finances requires precision. When your agent uses `create_activity` to log a `TimeEntry` or `ExpenseEntry`, Pydantic AI validates that the `quantity` is an integer representing seconds and the date is correctly formatted. No more bad data in your billing system. The same goes for invoicing. An agent can `list_bills` to find outstanding payments, and when it uses `get_bill` to inspect a specific one, you're guaranteed to get properly typed data for line items, totals, and due dates. If the API ever changes or returns something unexpected, your agent stops instead of processing bad financial data.

Setup guide

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

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

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

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Common questions about Clio MCP in Pydantic AI

When your agent gets data from Clio, Pydantic AI automatically validates it against a predefined schema. If a response from a tool like `get_matter` is missing a required field, it will raise an error instead of letting your agent proceed with incomplete data.

Yes, your agent can call the `create_contact` tool. Pydantic AI helps ensure you provide the right arguments, like a `first_name` and `last_name` for a person, or setting the `type` to 'Company' for a business.

That's one of its main advantages. Pydantic AI is model-agnostic, so you can connect it to any local or private LLM. Your agent can still use all the Clio tools, and the data validation works exactly the same.

Choose it if correctness is your top priority. It's for developers who want to build agents that are less likely to fail silently or corrupt data. The runtime type validation is a safety net for your legal practice data.

Your agent's requests to Clio, which contain data like contact names, matter details, and financial records, are routed through a Vinkius sandbox. Each request is isolated and ephemeral. Your Vinkius token authenticates the connection, and no data is logged or stored by the server.

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