How to Use the HiFlow MCP in Pydantic AI

Q: How do I configure HiFlow with Pydantic AI?

Install pydantic-ai-slimmcp. Define an MCPToolset using your endpoint URL. Pass it into your agent via the toolsets array to enable automatic schema validation.

Q: Does Pydantic AI validate HiFlow invoice data automatically?

Yes. Every time the agent calls getinvoice or listinvoices, the framework checks the payload. It guarantees the output matches your predefined Pydantic models.

Q: Is MCPServerHTTP still the right class to use?

No. That class is deprecated. You must use the unified MCPToolset approach for external HTTP endpoints.

Bring strict Pydantic AI validation to your HiFlow billing and timesheet workflows.

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Setup HiFlow with Pydantic AI

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Perplexity

Type-safe HiFlow MCP Server operations

Silent API failures destroy production workflows. When your agent calls `get_invoice` or `get_estimate`, this framework validates the response against your exact Pydantic schemas. Any missing fields trigger an immediate validation error. You never have to worry about hallucinated data. If `get_job` returns a string where an integer should be, the agent fails loudly. This strict enforcement guarantees your downstream code only receives perfectly structured information from the MCP Server.

Manage customers with guaranteed correctness

Creating client records requires precision. The `create_customer` tool plugs directly into your agent logic. It ensures every required field exists before attempting the POST request. Reading data follows the same strict rules. Calling `get_customer` or `list_customers` maps the JSON payload straight into your Python objects. Developers get full autocompletion in the IDE and absolute confidence in the data shape.

Model-agnostic timesheet tracking

You might start with OpenAI and switch to Anthropic later. Because this framework is model-agnostic, your `list_timesheets` implementation remains untouched. The agent pulls the hours regardless of which LLM drives the reasoning. Setup is straightforward using the unified `MCPToolset` class. Just pass your HTTP endpoint and assign the toolset to your agent. From there, it can run `get_activity` or `get_me` across any supported model provider.

Setup guide

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

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

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

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Common questions about HiFlow MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Define an `MCPToolset` using your endpoint URL. Pass it into your agent via the `toolsets` array to enable automatic schema validation.

Yes. Every time the agent calls `get_invoice` or `list_invoices`, the framework checks the payload. It guarantees the output matches your predefined Pydantic models.

You likely encountered a strict validation error. If the API returns unexpected data from `get_estimate`, the framework fails loudly to prevent silent corruption. Check your schema definitions.

No. That class is deprecated. You must use the unified `MCPToolset` approach for external HTTP endpoints.

Job descriptions and customer details process through an ephemeral sandbox. The system requires a single endpoint token and drops the connection immediately after execution. No user logs sit on the middleware servers.

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