How to Use the KPA Flex MCP in Pydantic AI

Type-safe KPA Flex integration for Pydantic AI. Validate every compliance data point before your agent acts.

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Connect KPA Flex MCP to Pydantic AI

Create your Vinkius account to connect KPA Flex 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 KPA Flex with Pydantic AI

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Strict data validation for Pydantic AI

Every response from `list_company_assets` or `list_ehs_inspections` gets validated against Pydantic models. Your agent never operates on malformed data. If the API returns unexpected fields, the process halts immediately. This prevents the silent errors common in less structured agent setups.

Type-safe inspection auditing in Pydantic AI

Fetch `get_inspection_details` and ensure it fits your expected format perfectly. Your agent code stays clean and predictable. It removes the need for manual type checking in your agent logic. You define the schema, and the server ensures the KPA Flex data matches it.

Reliable training tracking for Pydantic AI

Audit employee records using `list_safety_trainings` with complete confidence. The agent receives structured data that is ready for immediate processing. It handles the heavy lifting of parsing complex API responses. You get clean, validated objects every time you call a tool.

Setup guide

Set up KPA Flex 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": {
        "kpa-flex-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about KPA Flex MCP in Pydantic AI

You get runtime safety. Because the server tools are mapped to Pydantic models, your agent cannot hallucinate or process garbage data from the KPA Flex API.

Data is validated at the boundary. Only records that match your defined Pydantic structures are passed to the agent, keeping your internal state clean.

No. The MCP Server acts as the source of truth for tool definitions. You must connect your Pydantic AI agent to the running server instance.

Yes. If the API structure shifts, your Pydantic models will catch the mismatch. It alerts you to the change rather than letting the agent fail silently.

Yes. The connection uses standard transport security. Once the records reach your agent, they are stored only in your validated, local memory space.

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