How to Use the Qualified.io MCP in Pydantic AI

Q: How does the tool schema validation protect my hiring pipeline?

It ensures that arguments passed to tools like createassessment or invitecandidates strictly match the expected types, avoiding raw API errors and broken candidate flows.

Build type-safe hiring workflows with Pydantic AI agents that validate every Qualified.io candidate test result at runtime.

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…and any MCP-compatible client

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Connect Qualified.io MCP to Pydantic AI

Create your Vinkius account to connect Qualified.io to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Qualified.io with Pydantic AI

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Trigger validated candidate invites

Stop worrying about malformed API payloads breaking your hiring funnel by using this MCP Server with Pydantic AI to validate every candidate invitation sent via `invite_candidates` or `invite_candidates_via_cohort` against strict schemas. If an email address is malformed or a cohort ID is invalid, the Pydantic AI framework catches the error instantly, preventing broken invitations and ensuring a clean candidate experience.

Parse test results with Pydantic AI

Raw Qualified.io test data can be unpredictable, but your Pydantic AI agent pulls candidate submissions using `get_assessment_result` or `get_assessment_result_exhibit` and validates the structure before running any automated grading logic. Once validated, the Pydantic AI agent writes structured reviews back to the platform using `create_assessment_result_review` or `update_assessment_result_review` to guarantee that your hiring dashboard only contains clean, well-formatted interview feedback.

Manage your test catalog securely

Maintain absolute control over your Qualified.io challenge library by letting your Pydantic AI agent query your active tests using `list_assessments` and verify their status before pushing updates with `publish_assessment` or `unpublish_assessment`. If a test needs to be retired, the Pydantic AI agent safely executes `archive_assessment` or `unarchive_assessment` with runtime validation that ensures your agent never attempts to archive a non-existent test.

Setup guide

Set up Qualified.io 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": {
        "qualifiedio-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Qualified.io. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

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Common questions about Qualified.io MCP in Pydantic AI

Initialize the MCPToolset with your Vinkius HTTP endpoint URL. Pass this toolset instance directly into the toolsets parameter of your Pydantic AI Agent constructor to register all 20 API tools.

The Pydantic AI framework will raise a validation error immediately. This prevents your agent from making decisions based on corrupted or incomplete candidate profile data.

Yes, your Pydantic AI agent can fetch failed test metrics via get_assessment_result and safely execute schedule_retry_assessment_result to let candidates retake their coding challenges.

It ensures that arguments passed to tools like create_assessment or invite_candidates strictly match the expected types, avoiding raw API errors and broken candidate flows.

Vinkius uses secure, ephemeral V8 isolates to route all API calls. Your candidate emails and test scores are never stored on the proxy, ensuring complete data privacy and compliance with enterprise security standards.

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