How to Use the Custify MCP in Pydantic AI

Q: How do I initialize the Custify MCP Server with Pydantic AI?

Install pydantic-ai-slimmcp via pip. Create an MCPToolset pointing to your Vinkius HTTP URL and pass it to your Agent's toolsets array.

Enforce strict type validation on Custify metrics using Pydantic AI to build reliable customer success workflows.

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Connect Custify MCP to Pydantic AI

Create your Vinkius account to connect Custify 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 Custify with Pydantic AI

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Perplexity

Validate Health Scores in Pydantic AI

The `list_health_scores` tool extracts churn risk indicators and forces them through your defined schemas. If the API returns a string instead of an integer for a health metric, your agent fails loudly before executing bad logic. This exactness prevents silent data corruption. You can pull `get_company_details` knowing the resulting payload will perfectly match your internal models, regardless of whether you run OpenAI, Anthropic, or a local LLM.

Type-Safe Customer Event Tracking

Logging user behavior demands precise payload structures. Your agent calls `track_user_event` to push telemetry data, and the framework guarantees every required field exists before the request leaves your server. Model-agnostic validation keeps the pipeline stable. When the agent queries `list_user_segments`, it parses the response into strict Python objects, eliminating hallucinated fields from your retention reports.

Manage Profiles with the MCP Server

Adding new contacts requires accurate data entry. The `create_person` function accepts validated inputs from your agent, ensuring no malformed email addresses or missing names enter your customer success platform. Reading back that data is just as secure. The system executes `list_people` and maps the raw API response directly to your Pydantic classes, making the data instantly safe to use in downstream production code.

Setup guide

Set up Custify 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": {
        "custify-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Custify MCP in Pydantic AI

Install pydantic-ai-slim[mcp] via pip. Create an MCPToolset pointing to your Vinkius HTTP URL and pass it to your Agent's toolsets array.

The framework validates every single output against your Pydantic schemas at runtime. If a tool returns unexpected data types, the agent throws a validation error immediately.

The framework is completely model-agnostic. You can query get_person_details using Gemini, Claude, or a local Llama instance without changing your code.

No, that class is deprecated. You must use the unified MCPToolset approach with the HTTP string to connect to the external server.

Vinkius processes your health scores, user events, and contact lists inside an ephemeral V8 Isolate Sandbox. The zero-trust architecture ensures no private information persists after the tool call completes.

Use it with your favorite AI tools

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