How to Use the Yida MCP in Pydantic AI

Q: What happens if I try to update a record in Yida using Pydantic AI?

The agent uses updaterecord, but the result is validated. If Yida returns data that doesn't match your expected model, the agent throws an error immediately. You get certainty over speed.

Q: Can I list all application users with Pydantic AI and what data do I get?

Yes, use listusers. The tool provides user accounts, which are structured according to the server's schema. This means your Python agent receives predictable, typed data.

Q: How do I check what fields exist on a form in Yida using Pydantic AI?

You call getformschema. This pulls the definition of the form's fields, which you can then use to build your data structures. The output is always correctly typed.

Q: If I need to delete a record in Yida using Pydantic AI, what tools are involved?

You use the deleterecord tool. This operation requires you to specify the exact ID of the form record you want gone, ensuring only targeted deletions occur.

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Connect Yida MCP to Pydantic AI

Create your Vinkius account to connect Yida 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 Yida with Pydantic AI

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Type-Safe Record Creation on MCP Server

When you use `create_record`, your agent doesn't risk silent failures. Since the response is validated against Pydantic models, if Yida sends unexpected data, the agent fails loud—no corrupted fields. This guarantees that when building production systems with Yida, the input structure always matches what your code expects.

Listing and Retrieving Workflow Metadata via MCP Server

To track a process, you can get the full history using `get_instance_timeline`. This data is validated instantly. You're guaranteed that every timestamp and status change reported by Yida conforms to your defined Python model. It keeps complex workflow tracking reliable, preventing runtime errors from malformed responses.

Listing all forms in Yida using Pydantic AI

The `list_forms` tool provides a list of every form in the Yida application. Because the output is typed, you know exactly what data structure to expect for each listed form. This level of correctness saves time and debugging headaches when building complex multi-step automation.

Setup guide

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

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

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

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Common questions about Yida MCP in Pydantic AI

The agent uses `update_record`, but the result is validated. If Yida returns data that doesn't match your expected model, the agent throws an error immediately. You get certainty over speed.

Yes, use `list_users`. The tool provides user accounts, which are structured according to the server's schema. This means your Python agent receives predictable, typed data.

The server handles Form Records, Workflow Instances, and Application Users. All these data types are exposed with strict schema validation, giving you reliable payloads for your Python application.

You call `get_form_schema`. This pulls the definition of the form's fields, which you can then use to build your data structures. The output is always correctly typed.

You use the `delete_record` tool. This operation requires you to specify the exact ID of the form record you want gone, ensuring only targeted deletions occur.

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