How to Use the Clientify MCP in Pydantic AI

Strictly type-safe sales automation. Use Clientify with Pydantic AI for reliable CRM record management.

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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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Pydantic AI models for Clientify records

Every response from `get_contact` is validated against your Pydantic schema. If the CRM returns weird data, the agent errors out before it can act on bad info. Update records safely with `update_contact` using typed inputs. This ensures your CRM database remains clean and free of corrupted entries.

Type-safe deal tracking in Pydantic AI

Invoke `create_deal` to add new opportunities to your pipeline. The tool validates the deal amount and status fields before sending them to your CRM. List your active deals with `list_deals` to get a structured view of your sales progress. The agent enforces strict types on the returned opportunity objects.

Validating Clientify tasks in Pydantic AI

Use `create_activity` to log your work with full type safety. The agent checks that every required field is present before attempting to save the task. Query `list_activities` to see what is on your plate. Pydantic AI ensures the data matches your expectations, preventing runtime crashes in your agent logic.

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-alternative-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

The server forces every response into a Pydantic model. This means your agent won't hallucinate data fields that don't exist in your CRM.

Install the pydantic-ai-slim package to get the MCP support. You define the toolset and pass it to your agent, which handles the validation automatically.

The agent catches validation errors immediately if the CRM returns unexpected structures. This keeps your application logic stable rather than failing silently.

Yes, this framework is model-agnostic. You can pair local LLMs with this MCP server to keep your CRM data processing entirely off-cloud.

The server uses runtime validation to ensure data integrity during transmission. Your CRM records are processed within a zero-trust environment managed by Vinkius.

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