How to Use the Ashby MCP in Pydantic AI

Build type-safe recruiting pipelines using Pydantic AI to validate Ashby data at runtime.

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Connect Ashby MCP to Pydantic AI

Create your Vinkius account to connect Ashby 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 Ashby with Pydantic AI

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Validate Ashby schemas with Pydantic AI

This MCP Server ensures every piece of candidate data matches your strict type definitions by running `get_candidate_info`. If a resume or email field is malformed, the system catches it instantly. Pydantic AI forces your agent to fail loudly if the API returns unexpected structures. This prevents corrupt candidate profiles from entering your database.

Query jobs with strict type safety

The server pulls structured job listings using `list_jobs` to verify open roles against your internal databases. Every response is validated against runtime models to guarantee clean data. When searching for specific positions, `search_jobs` filters results with zero risk of silent parsing errors. You get predictable outputs every time your agent runs a query.

Monitor interviews without silent failures

Your system tracks scheduled loops using `list_interviews` to keep your MCP coordination clean. The tool returns structured meeting times that parse directly into Python datetime objects. If you need to dive deeper, `get_application_info` fetches the associated application details. The framework guarantees that every nested field matches your expected schema.

Setup guide

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

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

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

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Common questions about Ashby MCP in Pydantic AI

Initialize the MCP tools using MCPToolset with your Vinkius HTTP URL and pass it to your Agent's toolsets parameter. This registers all recruiting tools under strict runtime validation.

Pydantic AI validates the response against the tool schema. If a field like candidate email is missing, the framework raises a validation error instead of passing bad data to your LLM.

Yes, the toolset is model-agnostic. You can use it with Ollama, Anthropic, or OpenAI while maintaining the exact same validation rules for your recruiting data.

No, that class is deprecated. You should use the unified MCPToolset class to connect your external MCP Server over HTTP or SSE.

Your candidate profiles, interview feedback, and application histories are processed inside secure, ephemeral V8 isolates. Vinkius handles the API handshake, keeping your Ashby token isolated from the public LLM provider.

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