How to Use the UnifyApps MCP in Pydantic AI

Q: How do I get integration details using UnifyApps and Pydantic AI?

Use the getintegrationdetails tool. Since your agent validates results with Pydantic, you're guaranteed that the returned data matches the expected schema, preventing runtime errors.

Q: Can Pydantic AI reliably track UnifyApps flows?

Yes. The tools listautomationflows and listflowexecutions provide structured data about workflows. Your agent ensures this execution history is always parsed correctly, regardless of minor API changes.

Q: What does UnifyApps offer for agent management with Pydantic AI?

The tool listaiagents lets your agent list all configured agents. Because this response is validated by Pydantic, you know exactly what fields to expect and use.

Q: Does UnifyApps provide comprehensive data? (UnifyApps and Pydantic AI)

It provides structured data via tools like listintegrations and listactiveconnections. The value here is the guaranteed correctness of that data, not just volume.

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Connect UnifyApps MCP to Pydantic AI

Create your Vinkius account to connect UnifyApps 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 UnifyApps with Pydantic AI

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Validate all configured integrations.

The `list_integrations` tool pulls a list of connected services. Because your agent uses Pydantic validation, it knows the exact schema for every integration returned by UnifyApps. It also provides `get_integration_details`, allowing you to validate complex settings against strict Python models before committing any action.

Check connection status reliably.

`list_active_connections` retrieves account connections. If the API returns unexpected data (say, a missing 'status' field), your agent fails loud with a validation error—no silent corruption. Similarly, `list_ai_agents` ensures that every returned agent object conforms to your expected structure.

Audit and list automation flows.

`list_automation_flows` gives you the full set of defined workflows. Your Pydantic-validated agent handles this data type safely, ensuring consistency across all runs. When checking execution history with `list_flow_executions`, every record is validated against a strict schema for reliable auditing.

Setup guide

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

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

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

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Common questions about UnifyApps MCP in Pydantic AI

Use the `get_integration_details` tool. Since your agent validates results with Pydantic, you're guaranteed that the returned data matches the expected schema, preventing runtime errors.

Yes. The tools `list_automation_flows` and `list_flow_executions` provide structured data about workflows. Your agent ensures this execution history is always parsed correctly, regardless of minor API changes.

The tool `list_ai_agents` lets your agent list all configured agents. Because this response is validated by Pydantic, you know exactly what fields to expect and use.

It provides structured data via tools like `list_integrations` and `list_active_connections`. The value here is the guaranteed correctness of that data, not just volume.

UnifyApps exposes connection status and flow definitions. Your agent reads this metadata, such as which accounts are connected via `list_active_connections`, but it doesn't handle raw user content.

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