How to Use the Front MCP in CrewAI
Deploy autonomous agent crews to manage your Front inboxes. Let them monitor, triage, and reply to conversations 24/7.
Works with every AI agent you already use
…and any MCP-compatible client
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.
Build an Autonomous Support Team
CrewAI lets you assign roles. Create a 'Triage Agent' that constantly runs `list_inbox_threads` on your main support inbox. When it finds a new conversation, it passes the ID to a 'Context Agent'. The 'Context Agent' then uses `get_conversation_details` and `list_conversation_messages` to understand the issue. Finally, it hands off to an 'Action Agent' that either drafts a response with `send_inbox_reply` or escalates by using `update_conversation_status` to assign it to a human.
Monitor and Respond with Specialized Agents
You can set up a 'Monitoring Agent' whose only job is to watch for negative sentiment. It can use `search_conversations` to find conversations with angry keywords. Once found, it triggers an alert for a human to review. This division of labor is what makes CrewAI different. Your agents collaborate. One agent can pull a list of teammates with `list_inbox_teammates`, while another uses that data to decide who gets the next high-priority ticket. This MCP server provides all the tools they need to work together on Front.
Automate Cross-Inbox Operations
Your agent crew isn't limited to a single inbox. A 'Router Agent' can use `list_shared_inboxes` to get a map of your whole Front setup. It can then move conversations from a general inbox to a specialized one, like 'Billing' or 'Sales'. This allows for complex, autonomous workflows. For example, if a message in the support inbox contains pricing questions, the crew can automatically move it to the sales inbox and assign it to a sales rep, all without any human touching it.
Set up Front MCP in CrewAI
Prerequisites
- Python 3.10+ installed
-
crewaipackage (pip install crewai) - Active Vinkius subscription with a valid endpoint token
- 1
Install CrewAI
Run
pip install crewaito install the framework. MCP support is built-in via themcpsparameter. - 2
Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the
mcpslist. 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
Crewwith your agent and tasks. Callcrew.kickoff()— the agent will automatically invoke Front tools as needed.
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) Prerequisites
- Python 3.10+ installed
-
crewai+crewai-toolspackages - Active Vinkius subscription with a valid endpoint token
- 1
Install dependencies
Run
pip install crewai crewai-tools. TheMCPServerAdapterhandles lifecycle management and tool conversion. - 2
Connect with MCPServerAdapter
Use
MCPServerAdapteras a context manager withSseServerParameterspointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle. - 3
Assign tools and run
Pass the returned
mcp_toolsto your agent'stoolsparameter. The adapter converts MCP tools to nativeBaseToolobjects compatible with all CrewAI agents.
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) 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.
Why Choose Vinkius
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Real-time monitoring
Live
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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.
Built-in savings
60%
lower AI costs
Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.
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One
place for every integration
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Common questions about Front MCP in CrewAI
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