How to Use the Clientify MCP in Pydantic AI

Q: Is it hard to connect Pydantic AI to the Clientify MCP server?

It's minimal. After installing the library, you create an MCPToolset instance with your Vinkius MCP server URL. Then you pass that toolset to your Agent when you create it.

Get type-safe, validated access to your Clientify CRM data in any Python agent with Pydantic AI. No more silent failures.

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Connect Clientify MCP to Pydantic AI

Create your Vinkius account to connect Clientify 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 Clientify with Pydantic AI

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Enforce Strict Data Contracts

This MCP Server gives your agent functions to interact with Clientify, like `get_contact_crm_details` and `get_deal_details`. When your agent calls one of these, it gets a JSON response back. Here's the Pydantic AI difference: that JSON is immediately parsed and validated against a Pydantic model. If the Clientify API ever changes or returns something unexpected, your code raises a `ValidationError` instantly. You'll never process corrupted or incomplete CRM data by accident.

Build Model-Agnostic CRM Agents

Pydantic AI lets you choose your LLM. You can use this Clientify connection with an agent powered by OpenAI, Anthropic, Gemini, or even a local model running on your own machine. The tools, like `list_clientify_companies` and `list_sales_pipelines`, work exactly the same way. This frees you from vendor lock-in. You can swap out the underlying model for one that's cheaper, faster, or better at a specific task without rewriting any of your data-fetching logic. The Pydantic AI toolset for Clientify remains constant.

A Reliable MCP Server for Pydantic AI

Your agent can list all contacts with `list_clientify_contacts` or check recent work with `list_crm_activities`. Every piece of data returned by these tools is guaranteed to match the expected structure. This focus on correctness makes Pydantic AI a great choice for building reliable internal tools. You can create a script that audits sales deals from Clientify, confident that a surprise API change won't cause it to fail silently or produce bad reports.

Setup guide

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

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

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

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Common questions about Clientify MCP in Pydantic AI

When your agent gets data from the Clientify MCP Server, Pydantic AI automatically validates it against a corresponding Pydantic model. If any field is missing, has the wrong type, or doesn't match the expected structure, your code will raise an error immediately.

Yes. Pydantic AI is model-agnostic. You can configure it to use a local model, like Llama or Mistral, to drive the agent that interacts with your Clientify data.

Correctness. With Pydantic AI, you know for a fact that the Clientify data your agent is working with is well-formed and matches the structure you expect. No silent data corruption, ever.

It's minimal. After installing the library, you create an `MCPToolset` instance with your Vinkius MCP server URL. Then you pass that toolset to your `Agent` when you create it.

Your Clientify data—contacts, deals, companies—is requested by your agent and passes through the Vinkius MCP server, which runs in an isolated, ephemeral instance. The connection is encrypted and authenticated. No data is ever written to disk or stored by the server; it's just a pass-through validation layer.

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