How to Use the Ashby MCP in Pydantic AI

Q: Does Pydantic AI validate Ashby API responses at runtime?

Yes, it checks every field from tools like getashbyapplication against your strict Pydantic models.

Type-safe recruiting pipelines with Pydantic AI and Ashby.

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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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Type-Safe MCP Server Validation

The `get_ashby_candidate` tool retrieves candidate profiles and passes them directly into Pydantic AI's validation engine. If the ATS API returns unexpected fields or missing data, the framework fails loudly before corrupting your internal state. You define the exact schema you expect from your hiring database. This runtime validation guarantees that your downstream Python code never processes a candidate record with an invalid email or missing name.

Structured Application Fetching

The `get_ashby_application` tool pulls detailed application histories into strongly typed Python models. Pydantic AI parses the nested JSON payload, mapping jobs and candidate stages to strict Python types. If a recruiter updates a custom field in Ashby, your agent won't silently ignore the change. The Pydantic AI model-agnostic validator flags any schema drift immediately so you can update your code.

Validated Interview Tracking

The `list_ashby_interviews` tool retrieves scheduled panels and feedback deadlines for your pipeline validator. Pydantic AI enforces type correctness on every date string and user ID returned by the MCP Server. This strictness ensures your scheduling agent never writes bad dates to your calendar integrations. You get reliable, predictable automation because the framework blocks invalid payloads at the boundary.

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-alternative-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

Use the MCPToolset constructor with your Vinkius HTTP endpoint and pass it to your agent.

Yes, it checks every field from tools like get_ashby_application against your strict Pydantic models.

The framework throws a validation error immediately, preventing your agent from executing further with bad data.

Yes, the framework is model-agnostic, letting you use local or commercial LLMs with the MCP Server.

Candidate files and feedback are handled in ephemeral isolates, ensuring no data is stored on Vinkius.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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