How to Use the Serviceform MCP in Pydantic AI

Q: How do I get lead details using Serviceform with Pydantic AI?

Call getlead to fetch the required data. Because of Pydantic's runtime validation, you know that the resulting object will strictly adhere to your defined schema. You won't run into unexpected fields.

Q: Can I list active forms using Serviceform with Pydantic AI?

Yes, use listforms. The tool provides a clean list of every form running on the site. This helps your agent confirm data collection capabilities while maintaining type safety.

Q: What JSON criteria can I pass to Serviceform with Pydantic AI when listing chats?

You pass specific search parameters as a JSON string to listchats. The strength of the MCP Server is that even if the underlying API structure changes, your agent won't crash silently because of type validation.

Q: Does Serviceform with Pydantic AI help list configured spaces?

Run listspaces to get an inventory of 'flex spaces.' The Pydantic framework wraps this data, guaranteeing that the space names and IDs are correctly typed for your agent's subsequent actions.

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Setup Serviceform with Pydantic AI

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Want to review past conversations? Use `list_chats` by passing criteria as a JSON string. Because of the type-safe structure, if the API returns unexpected data, your agent fails loudly. The Pydantic framework ensures that when you process this historical data, every field matches the expected schema exactly.

See all site forms

Start by listing what's available. Call `list_forms` to get a list of active forms running on your site. This is critical for validating which data inputs are legitimate. If the API structure changes, Pydantic catches it instantly, meaning you never run against corrupted or unexpected fields.

Fetch structured lead profiles

The `get_lead` tool retrieves specific lead details. This is a high-value operation, and using Pydantic ensures the resulting object has predictable types for your agent to use. This prevents silent corruption; if the API misreports data, your agent fails immediately with a validation error.

Setup guide

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

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

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

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Common questions about Serviceform MCP in Pydantic AI

Call `get_lead` to fetch the required data. Because of Pydantic's runtime validation, you know that the resulting object will strictly adhere to your defined schema. You won't run into unexpected fields.

Yes, use `list_forms`. The tool provides a clean list of every form running on the site. This helps your agent confirm data collection capabilities while maintaining type safety.

You pass specific search parameters as a JSON string to `list_chats`. The strength of the MCP Server is that even if the underlying API structure changes, your agent won't crash silently because of type validation.

Run `list_spaces` to get an inventory of 'flex spaces.' The Pydantic framework wraps this data, guaranteeing that the space names and IDs are correctly typed for your agent's subsequent actions.

The server touches PII (Personally Identifiable Information) through the `get_lead` and `list_chats` tools. The robust validation of Pydantic ensures that when you handle this sensitive data, the structure is always predictable.

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