How to Use the Fountain MCP in CrewAI

Q: How do I connect the Fountain MCP Server to CrewAI?

Pass your Vinkius endpoint URL directly into the mcps array when defining your agent. CrewAI handles the HTTP transport automatically.

Q: Can I restrict which Fountain tools my CrewAI agents can access over MCP?

Yes. Import MCPServerHTTP from crewai.mcp and apply a toolfilter. You can give one agent listapplicants and another agent listhiringgoals.

Deploy autonomous HR teams in CrewAI to screen Fountain applicants, track hiring goals, and manage interview schedules.

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Connect Fountain MCP to CrewAI

Create your Vinkius account to connect Fountain to CrewAI 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 Fountain with CrewAI

Ask AI about this MCP

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Perplexity

Build Applicant Screening Crews

The `list_applicants` tool provides a raw feed of new candidates for your researcher agent to evaluate. This agent pulls the initial list through the MCP protocol and immediately hands the IDs to an analyst agent running `get_applicant`. A third moderator agent then reviews the findings and writes a summary. It pulls historical context using `list_applicant_notes` to ensure the new assessment aligns with previous recruiter feedback.

Manage Funnels with CrewAI MCP Server

The `list_funnels` and `list_funnel_stages` tools let your crew map the exact state of your hiring pipeline. A dedicated monitoring agent watches these stages for bottlenecks. When candidate flow drops, the monitor alerts an operations agent. That agent checks `list_openings` to verify the roles are actually live, diagnosing the problem without human intervention.

Track Quotas and Worker Deployment

The `list_hiring_goals` tool gives your manager agent the baseline targets for the quarter. It delegates a task to a subordinate agent to count active employees using `list_workers`. If the crew detects a shortfall, they pull `list_interview_sessions` to see if enough candidates are scheduled to close the gap. The agents execute this sequence hierarchically, reporting only the final deficit to your HR director.

Setup guide

Set up Fountain MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Fountain tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Fountain Analyst",
    goal="Access and analyze Fountain data via MCP.",
    backstory="Expert analyst with direct Fountain access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Fountain transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Fountain Analyst",
        goal="Access and analyze Fountain data via MCP.",
        backstory="Expert analyst with direct Fountain access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Fountain transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Fountain. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Fountain MCP in CrewAI

Pass your Vinkius endpoint URL directly into the `mcps` array when defining your agent. CrewAI handles the HTTP transport automatically.

Yes. Import `MCPServerHTTP` from `crewai.mcp` and apply a `tool_filter`. You can give one agent `list_applicants` and another agent `list_hiring_goals`.

CrewAI agents share memory natively. When your researcher agent calls `get_applicant`, the resulting candidate profile becomes available to the rest of the crew for analysis.

You can set up a sequential crew that runs on a cron job. It will call `list_interview_sessions` every hour and alert your team if schedules change.

The server accesses personally identifiable candidate profiles and internal recruiter notes via `get_applicant`. Vinkius secures this connection using ephemeral containers that spin down immediately after your crew finishes its task.

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