How do I get an API Token for Fountain?

You can find or generate your API token in your Fountain dashboard under Company Settings > Developer Settings.

Does Fountain support bulk actions?

Yes, the Fountain API is designed for scale. While this MCP server focuses on management and retrieval, bulk advance and transitions are supported by the core platform.

Can I see interview schedules via the agent?

Yes, the 'list_interview_sessions' tool allows you to retrieve all scheduled and past interview sessions directly from your account.

Is worker data accessible after hire?

Yes, you can use the 'list_workers' tool to access profiles of individuals who have successfully transitioned to 'hired' status.

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

Name: Fountain
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 Fountain 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 Fountain "
            "(12 tools)."
        ),
    )

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

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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 Fountain MCP Server

Connect your Fountain account to any AI agent to automate your high-volume hiring and applicant lifecycle management through the Model Context Protocol (MCP). Fountain is designed specifically for frontline workforce management, allowing you to streamline every stage from sourcing to onboarding. This MCP server enables you to manage your applicant funnels, track hiring progress, and oversee worker profiles directly through natural conversation.

Pydantic AI validates every Fountain 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.

Key Features

Applicant Oversight — List all applicants, search by email or funnel, and fetch detailed profiles including transition history.
Funnel & Stage Management — Access and list your hiring funnels and specific stages to understand your pipeline health.
Hiring Goal Tracking — Monitor your progress against specific hiring targets and performance metrics.
Opening Management — List all active job openings and fetch detailed metadata for specific positions.
Interview Coordination — List and oversee scheduled interview sessions across your organization.
Worker Profiles — Access metadata for individuals who have successfully completed the hiring process.
Sourcing Insights — Monitor published job posts across various channels to optimize your recruitment reach.

The Fountain 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 Fountain to Pydantic AI via MCP

Follow these steps to integrate the Fountain 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 Fountain with type-safe schemas

Why Use Pydantic AI with the Fountain MCP Server

Pydantic AI provides unique advantages when paired with Fountain 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 Fountain integration code

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

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

Fountain + Pydantic AI Use Cases

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

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

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

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

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

Fountain MCP Tools for Pydantic AI (12)

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

get_account_details

Get organization attributes

get_applicant

Get applicant details

get_opening_details

Get opening metadata

list_applicant_notes

Get applicant discussion

list_applicants

List job applicants

list_funnel_stages

List stages in a funnel

list_funnels

List hiring funnels

list_hiring_goals

List hiring targets

list_interview_sessions

List scheduled interviews

list_job_posts

List published job posts

list_openings

List active job openings

list_workers

List hired workers

Example Prompts for Fountain in Pydantic AI

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

"List all active job openings in Fountain."

"Show me the last 10 applicants for the 'Delivery' funnel."

"Get the hiring goals summary for this quarter."

Troubleshooting Fountain MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Fountain + Pydantic AI FAQ

Common questions about integrating Fountain 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 Fountain MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.