How to Use the Mainstay (AdmitHub) MCP in CrewAI

Q: How does CrewAI protect custom student metadata from exposure?

When querying listcustomfields, all data transitions through a secure, single-use V8 sandbox. Your student metadata is processed in-memory and never written to permanent storage.

Run autonomous teams of specialized AI agents to manage student outreach using CrewAI and this Mainstay (AdmitHub) MCP Server.

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Connect Mainstay (AdmitHub) MCP to CrewAI

Create your Vinkius account to connect Mainstay (AdmitHub) 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 Mainstay (AdmitHub) with CrewAI

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Multi-Agent Campaign Coordination

This MCP Server uses `list_campaigns` to let your CrewAI agent teams monitor active outreach efforts. One agent analyzes campaign performance while another coordinates follow-up tasks. By sharing context through CrewAI's memory system, the research agent can pass campaign IDs to the action agent. This allows the team to coordinate complex outreach strategies without manual handoffs.

Autonomous Student Profile Audits

This tool uses `list_contacts` to retrieve student records for autonomous auditing. A moderator agent scans the contact list, identifying records with missing information or outdated fields. The agent then calls `get_contact_details` to inspect individual profiles. If a record is incomplete, it flags the file and alerts the human coordinator to update the student's file.

Message Log Analysis and Escalation

This tool uses `list_messages` to analyze recent student interactions for urgent issues. A specialized sentiment agent reads the message logs using the MCP Server to detect students who are struggling or frustrated. Once detected, the agent uses `list_custom_fields` to find the assigned advisor's ID. It then escalates the issue, ensuring human advisors intervene when automated nudges fail.

Setup guide

Set up Mainstay (AdmitHub) 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 Mainstay (AdmitHub) tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Mainstay (AdmitHub) 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="Mainstay (AdmitHub) Analyst",
        goal="Access and analyze Mainstay (AdmitHub) data via MCP.",
        backstory="Expert analyst with direct Mainstay (AdmitHub) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Mainstay (AdmitHub) 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 Mainstay. 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 Mainstay (AdmitHub) MCP in CrewAI

CrewAI agents use a shared memory system. When one agent calls `get_contact_details`, the resulting student profile is stored in memory, allowing other agents to access it.

Yes. You can filter tools when defining your crew, ensuring only your designated coordinator agent can call sensitive tools like `list_contacts`.

The server supports standard transport methods, allowing your CrewAI Python environment to connect via Streamable HTTP or SSE endpoints.

Yes. You can schedule your crew to run sequentially or hierarchically, letting them pull records and analyze campaigns on a set schedule.

When querying `list_custom_fields`, all data transitions through a secure, single-use V8 sandbox. Your student metadata is processed in-memory and never written to permanent storage.

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