Can my agent list all candidates for a specific job in Greenhouse?

Yes. Use the 'list_candidates' tool with the 'job_id' parameter. The agent will fetch all candidates associated with that specific opening flawlessly.

How do I check the current hiring stage of an applicant via chat?

You can use the 'get_application' tool. Provide the Application ID, and the agent will return the full details, including the candidate's current stage in the hiring process natively.

Can I search for candidates who were added recently through the agent?

Absolutely. Use the 'list_candidates' tool with sorting or filtering parameters like 'created_after'. Your agent will retrieve the most recent additions to your Greenhouse database natively.

Greenhouse MCP Server for Pydantic AI 12 tools — connect in under 2 minutes

Name: Greenhouse
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 12 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Greenhouse through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Greenhouse "
            "(12 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Greenhouse?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Greenhouse MCP Server

Connect your Greenhouse Recruiting account to any AI agent and take control of your talent acquisition pipeline through natural conversation.

Pydantic AI validates every Greenhouse tool response against typed schemas, catching data inconsistencies at build time. Connect 12 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Candidate Oversight — List all candidates in your system and retrieve specific profile details natively
Pipeline Tracking — Monitor job applications and their current statuses across your active hiring processes flawlessly
Job Management — List and inspect job configurations, including hiring stages and department mappings synchronously
Team Coordination — Retrieve office and department structures to ensure your hiring data is aligned with organizational goals
User Auditing — List and verify user roles and access levels within your Greenhouse workspace natively

The Greenhouse MCP Server exposes 12 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Greenhouse to Pydantic AI via MCP

Follow these steps to integrate the Greenhouse MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 12 tools from Greenhouse with type-safe schemas

Why Use Pydantic AI with the Greenhouse MCP Server

Pydantic AI provides unique advantages when paired with Greenhouse through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Greenhouse integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Greenhouse connection logic from agent behavior for testable, maintainable code

Greenhouse + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Greenhouse MCP Server delivers measurable value.

Type-safe data pipelines: query Greenhouse with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Greenhouse tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Greenhouse and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Greenhouse responses and write comprehensive agent tests

Greenhouse MCP Tools for Pydantic AI (12)

These 12 tools become available when you connect Greenhouse to Pydantic AI via MCP:

create_candidate

Create a new candidate profile

get_application

Get details for a specific application

get_candidate

Get details for a specific candidate

get_job

Get details for a specific job

get_user

Get details for a specific user

list_applications

Retrieve job applications

list_candidates

List all candidates in Greenhouse

list_departments

List company departments

list_job_stages

List hiring stages for a specific job

list_jobs

List jobs in Greenhouse

list_offices

List company offices

list_users

List Greenhouse users

Example Prompts for Greenhouse in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Greenhouse immediately.

"List my active jobs in Greenhouse"

"Show me the profile for candidate ID 93021"

"What are the hiring stages for the 'Product Designer' job?"

Troubleshooting Greenhouse MCP Server with Pydantic AI

Common issues when connecting Greenhouse to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Greenhouse + Pydantic AI FAQ

Common questions about integrating Greenhouse MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Greenhouse MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.