How to Use the HrFlow.ai MCP in Pydantic AI

Force strict type validation on HrFlow.ai candidate data using Pydantic AI agents.

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Connect HrFlow.ai MCP to Pydantic AI

Create your Vinkius account to connect HrFlow.ai 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 HrFlow.ai with Pydantic AI

Ask AI about this MCP

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Perplexity

Guarantee resume data shape in Pydantic AI

Parsing resumes is notoriously messy. When your agent calls `parse_profile`, Pydantic AI forces the HrFlow output into strict data models. If a date format is wrong, the agent fails loudly instead of silently corrupting your database. This exactness extends to your talent pipelines. The `list_sources` and `list_boards` tools return predictable arrays. Your agent knows exactly what fields exist before it tries to map candidates to your internal systems.

Score candidates with runtime validation

You cannot afford hallucinations in hiring decisions. The `score_profiles` tool evaluates candidates against job requirements, and Pydantic AI ensures the resulting match score is an actual float. Bad data gets caught immediately. If an agent needs more context on a low score, it triggers `ask_profile`. The response is validated at runtime. Recruiters only see verified facts from the applicant's actual work history.

Query your HrFlow MCP Server safely

Building autonomous sourcing agents requires predictable search results. The `search_jobs` and `search_profiles` tools accept strict semantic filters. They return exact matches based on your predefined schemas. Once the agent identifies a prospect, it runs `unfold_profile` to extract their career timeline. Because you define the Pydantic model upfront, the agent extracts the exact progression steps without making up missing employment gaps.

Setup guide

Set up HrFlow.ai 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": {
        "hrflowai-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about HrFlow.ai MCP in Pydantic AI

Install the slim package with MCP support. Use the unified MCPToolset class and point it at the server URL.

The API likely returned a field shape that violated your Pydantic model. Check your schema definitions against the HrFlow documentation.

Yes. Pydantic AI is model-agnostic, so you can route the parsing and scoring tasks through any supported LLM.

It does. The MCPServerHTTP class is deprecated. Use the unified toolset for all external connections.

This MCP connection reads names, contact info, and job histories. Pydantic's strict models ensure no unexpected PII bleeds into your application logs unless you explicitly map those fields.

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