How do I get HrFlow API credentials?

You can find your X-API-KEY in the HrFlow dashboard under Settings > API. You also need the email address of your account.

Can I parse PDF resumes with this MCP?

Yes, the parse_profile tool allows you to provide a public URL to a resume file for AI parsing.

What is profile asking?

It's an AI feature that lets you ask questions about a candidate's profile in plain English and get intelligent answers based on their data.

HrFlow.ai MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: HrFlow.ai
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect HrFlow.ai through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to HrFlow.ai "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in HrFlow.ai?"
    )
    print(result.data)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About HrFlow.ai MCP Server

Empower your AI agents with HrFlow.ai's advanced talent acquisition capabilities. This MCP server allows you to parse resumes, search profiles and jobs with semantic filters, score candidates against job descriptions, and ask natural language questions about profiles. Ideal for automating recruitment workflows with AI-driven insights.

Pydantic AI validates every HrFlow.ai tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

The HrFlow.ai MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect HrFlow.ai to Pydantic AI via MCP

Follow these steps to integrate the HrFlow.ai MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from HrFlow.ai with type-safe schemas

Why Use Pydantic AI with the HrFlow.ai MCP Server

Pydantic AI provides unique advantages when paired with HrFlow.ai through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your HrFlow.ai integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your HrFlow.ai connection logic from agent behavior for testable, maintainable code

HrFlow.ai + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the HrFlow.ai MCP Server delivers measurable value.

Type-safe data pipelines: query HrFlow.ai with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple HrFlow.ai tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query HrFlow.ai and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock HrFlow.ai responses and write comprehensive agent tests

HrFlow.ai MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect HrFlow.ai to Pydantic AI via MCP:

ask_profile

Asks a natural language question about a specific profile

list_boards

Lists job boards

list_jobs

Lists jobs stored in HrFlow boards

list_profiles

Lists candidate profiles stored in HrFlow

list_sources

Lists profile sources

parse_profile

Parses a resume file into a structured profile

score_profiles

Scores candidate profiles against a specific job

search_jobs

Searches for jobs with semantic filters

search_profiles

Searches for profiles with semantic filters

unfold_profile

Analyzes and unfolds the career path of a profile

Example Prompts for HrFlow.ai in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with HrFlow.ai immediately.

"List the last 5 profiles added to my HrFlow source."

"Ask profile key 'abc-123' if they have experience with Kubernetes."

"Find jobs that match candidate 'john-doe-key'."

Troubleshooting HrFlow.ai MCP Server with Pydantic AI

Common issues when connecting HrFlow.ai to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

HrFlow.ai + Pydantic AI FAQ

Common questions about integrating HrFlow.ai MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your HrFlow.ai MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.