How to Use the Greenhouse MCP in Pydantic AI

Q: How do I register the Greenhouse MCP Server with Pydantic AI?

Install the library using pip install "pydantic-ai-slimmcp" and initialize the server. Use the unified MCPToolset constructor pointing to your Vinkius MCP Server endpoint, then pass it to your Agent via the toolsets parameter.

Q: Can I test my API connection status before running complex agent workflows?

Yes, you can call the getapistatus tool to check if your Greenhouse credentials are valid. This provides a fast, type-safe health check to verify your connection before starting candidate processing.

Build type-safe recruitment pipelines using Greenhouse and Pydantic AI.

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Connect Greenhouse MCP to Pydantic AI

Create your Vinkius account to connect Greenhouse 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 Greenhouse with Pydantic AI

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Validate candidate records in Pydantic AI

The `get_candidate_details` tool retrieves structured applicant information that is immediately validated against your Pydantic models. This ensures your agent never processes corrupt, incomplete, or malformed candidate profiles. If the API returns unexpected data, the system fails loudly and immediately. Run-time validation prevents silent errors from polluting your downstream hiring workflows and candidate databases.

Query jobs safely using this MCP Server

The `list_open_jobs` tool pulls active job postings and enforces strict type constraints on the returned payload. Your agent can confidently map applications to jobs without worrying about missing fields. By combining this with `list_departments` and `list_offices`, you get a guaranteed structure of your organization's hiring footprint. Every department and office ID will match your strict schema.

Secure pipeline updates with Pydantic AI

The `create_candidate` tool ensures that any new applicant profile created by your agent contains exactly the required fields. First name, last name, and company details are checked at the code level before submission using the Greenhouse MCP Server. When you need to transition candidates, `advance_application` and `reject_application` execute with strict type safety. This prevents invalid stage transitions or missing rejection reasons from breaking your pipeline.

Setup guide

Set up Greenhouse 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": {
        "greenhouse-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Greenhouse MCP in Pydantic AI

Install the library using `pip install "pydantic-ai-slim[mcp]"` and initialize the server. Use the unified MCPToolset constructor pointing to your Vinkius MCP Server endpoint, then pass it to your `Agent` via the `toolsets` parameter.

Pydantic AI will raise a validation error immediately rather than passing bad data to your agent. This guarantees that tools like `get_candidate_details` only return clean, structured data that matches your defined models.

Yes, this MCP Server integration supports both Streamable HTTP and SSE transports. You can choose the protocol that best fits your production deployment and network topology.

Yes, you can call the `get_api_status` tool to check if your Greenhouse credentials are valid. This provides a fast, type-safe health check to verify your connection before starting candidate processing.

Your candidate applicant profiles and hiring pipelines are processed in a zero-trust, ephemeral sandbox. Vinkius handles the authorization token securely, ensuring your sensitive Greenhouse data is never stored or exposed to external networks.

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