How to Use the DevSkiller MCP in Pydantic AI

Q: How do I add the DevSkiller MCP Server to Pydantic AI?

Install pydantic-ai-slimmcp. Define an MCPToolset with your HTTP URL and pass it to the toolsets argument of your Agent.

Q: Should I use MCPServerHTTP for the connection?

No. That class is deprecated. Always use the unified MCPToolset for defining your external server connections.

Integrate DevSkiller with Pydantic AI to enforce strict type validation on every candidate assessment and test invitation.

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Connect DevSkiller MCP to Pydantic AI

Create your Vinkius account to connect DevSkiller 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 DevSkiller with Pydantic AI

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Validate candidates with Pydantic AI

The DevSkiller MCP Server forces your hiring agent to respect strict data contracts. When your system calls `search_candidates_by_identity`, Pydantic AI validates the response against your internal schemas. If the API returns a malformed record, the agent fails loudly instead of hallucinating a candidate. You build pipelines that actually work in production. The agent uses `list_high_score_candidates` to find top performers, and the framework guarantees every returned score is a valid integer before your scheduling logic runs.

Issue test invitations safely

The DevSkiller integration lets your agent issue test invitations safely. Your agent checks `list_available_tests` to grab the correct ID, then executes `invite_candidate_to_test`. The type-safe environment ensures the email string and test ID match the required formats perfectly. The MCP setup uses the modern unified approach. Install the slim package, configure `MCPToolset` with your HTTP endpoint, and pass it to your agent. The deprecated `MCPServerHTTP` class is gone, leaving you with clean, predictable code.

Track recent activity across funnels

This MCP Server tracks recent activity across your entire hiring pipeline. Your agent pulls `list_recently_sent_invitations` to verify which candidates received their technical screens today. It groups the pending tests and updates your internal tracking system without missing a single row. You never worry about model lock-in. Because the MCP integration is model-agnostic, you can use Anthropic to evaluate `get_candidate_assessment_report` today and swap to a local model tomorrow. The tool execution remains identical.

Setup guide

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

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

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

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Common questions about DevSkiller MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Define an `MCPToolset` with your HTTP URL and pass it to the `toolsets` argument of your Agent.

The framework throws a runtime validation error. It refuses to process malformed responses from endpoints like `get_candidate_profile`, preventing downstream corruption.

Yes. The framework is entirely model-agnostic. Your local LLM can call `list_assessment_candidates` exactly like OpenAI or Gemini would.

No. That class is deprecated. Always use the unified `MCPToolset` for defining your external server connections.

The endpoints handle personally identifiable information like names and email addresses. The architecture relies on managed endpoints where authentication is isolated from the executing code, ensuring your agent only sees the specific JSON it requested.

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