How to Use the Mokaform MCP in Pydantic AI

Q: What is the correct way to connect Pydantic AI to the Mokaform MCP Server?

Install the slim package with pip install "pydantic-ai-slimmcp". Use the unified MCPToolset class with your HTTP endpoint, and pass it to your Agent's toolsets parameter.

Q: Does Pydantic AI validate the structure of forms fetched from Mokaform?

Yes. When the agent calls getform, the response is parsed directly into Pydantic models. This ensures that the dynamic branching logic complies with your application's expected types.

Q: How does Pydantic AI handle validation errors when querying Mokaform responses?

If getresponse returns data that doesn't match your Pydantic schema, the framework raises a validation error. This prevents your agent from processing corrupt or incomplete survey submissions.

Build type-safe survey pipelines with Pydantic AI to validate every Mokaform response at runtime.

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Setup Mokaform with Pydantic AI

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Strict Runtime Validation for Pydantic AI Forms

Avoid silent data corruption in your data pipelines. When your Pydantic AI agent pulls answers using `get_response` or `list_responses`, the framework validates the incoming JSON payload against your defined schemas. If a user submits malformed data or a field is missing, the system catches it immediately. You don't have to worry about your database failing due to unexpected survey formats.

Type-Safe Survey Creation and Updates

Building forms dynamically can be risky if schemas drift. By using `create_form` and `update_form` within the Pydantic AI toolset, you ensure that every question, branch, and logic rule matches your strict Python type hints. The agent cannot generate invalid form structures. The framework validates the tool inputs before sending them to the MCP Server, preventing API errors before they happen.

Model-Agnostic Workspace Mapping

You aren't locked into a single LLM provider. Use Pydantic AI to run your survey workflows using OpenAI, Anthropic, or local models while managing assets with `list_workspaces` and `list_forms` via our MCP Server. The framework ensures consistent data output regardless of the model you choose. Your agent can safely query form lists and clean up old data using `delete_response` without changing a line of integration code.

Setup guide

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

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

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

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Common questions about Mokaform MCP in Pydantic AI

Install the slim package with `pip install "pydantic-ai-slim[mcp]"`. Use the unified `MCPToolset` class with your HTTP endpoint, and pass it to your Agent's `toolsets` parameter.

Yes. When the agent calls `get_form`, the response is parsed directly into Pydantic models. This ensures that the dynamic branching logic complies with your application's expected types.

If `get_response` returns data that doesn't match your Pydantic schema, the framework raises a validation error. This prevents your agent from processing corrupt or incomplete survey submissions.

Yes, because Pydantic AI is model-agnostic. You can run a local model to call `list_workspaces` and organize your forms without sending any data to external LLM providers.

Your form schemas and logic rules are processed locally within your type-safe runtime. The MCP connection uses secure, token-authorized endpoints, keeping your proprietary configurations confidential.

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