How to Use the Front MCP in CrewAI

Q: How do I add the Front MCP Server to my CrewAI agents?

Pass the server URL directly into the mcps parameter when defining your CrewAI Agent. You can also use MCPServerHTTP for advanced tool filtering.

Q: How do I limit which Front tools a specific CrewAI agent can access?

Use the toolfilter option within CrewAI's MCP configuration. This prevents a research agent from accessing replytoconversation while allowing it to use getmessagecontent.

Q: Is our Front customer contact data safe during execution?

Yes. When agents pull contact records using getcontactinfo, the data stays within your local Python execution environment. Vinkius handles the underlying credentials securely, ensuring no third-party logs your customer's personal details.

Deploy specialized agent teams that collaborate to manage your Front shared inboxes.

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Connect Front MCP to CrewAI

Create your Vinkius account to connect Front 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 Front with CrewAI

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Triage Team Inboxes with CrewAI

The `list_shared_inboxes` tool identifies all active team queues so your CrewAI agents can divide and conquer. A triage agent can monitor incoming queues while a separate responder agent drafts replies to customer messages using this MCP Server. This multi-agent setup uses shared memory to pass inbox details between specialized roles. The triage agent flags high-priority items, letting the response agent focus strictly on crafting accurate messages.

Context-Aware Customer Support Teams

The `list_conversation_messages` tool pulls the entire history of a thread so your analysis agent has full context. This setup routes the historical thread through CrewAI's MCP integration, ensuring your agents read past interactions to understand the customer's problem before handing the task off. The next agent in the crew uses `get_message_content` to extract specific technical details from the latest email. This multi-step pipeline ensures your automated responses are based on actual history, not guesses.

Coordinate Escalations via CrewAI MCP Server

The `search_conversations_by_query` tool allows your supervisor agent to find related tickets across the entire history of your workspace. If a customer reports a recurring bug, the agent locates past occurrences to find existing workarounds. Once found, a support agent uses `reply_to_conversation` to send the resolution. This collaborative cycle resolves complex inquiries without requiring human intervention at every step.

Setup guide

Set up Front 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 Front tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Front 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 Front. 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 Front MCP in CrewAI

Pass the server URL directly into the `mcps` parameter when defining your CrewAI Agent. You can also use `MCPServerHTTP` for advanced tool filtering.

Yes. CrewAI's hierarchical or sequential execution models allow separate agents to call `get_contact_info` and `list_conversations` in parallel to build a complete customer profile.

Use the `tool_filter` option within CrewAI's MCP configuration. This prevents a research agent from accessing `reply_to_conversation` while allowing it to use `get_message_content`.

Yes. Your monitoring agent can continuously check threads and trigger `update_conversation_status` to archive resolved conversations as soon as the response agent finishes.

Yes. When agents pull contact records using `get_contact_info`, the data stays within your local Python execution environment. Vinkius handles the underlying credentials securely, ensuring no third-party logs your customer's personal details.

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