How to Use the Gallabox MCP in CrewAI

Q: How do I configure the Gallabox MCP Server for CrewAI agents?

Pass your endpoint URL directly into the agent's mcps array. CrewAI automatically connects, fetches the tool schemas, and makes them available to that specific agent.

Q: How do I filter specific tools for different roles?

Use MCPServerHTTP from the toolkit and apply a toolfilter. You can restrict your research agent to read-only tools like getchatdetails while only allowing the response agent to use messaging tools.

Deploy autonomous WhatsApp support teams in CrewAI using the Gallabox MCP Server.

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Connect Gallabox MCP to CrewAI

Create your Vinkius account to connect Gallabox 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 Gallabox with CrewAI

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Automate triage with a CrewAI MCP Server

The `list_active_chats` tool pulls the current queue of unassigned WhatsApp conversations. Your monitor agent scans this list constantly. It reads the initial user messages and categorizes the intent based on keywords and context. After categorization, the agent executes `list_support_teams`. It matches the customer's problem to the specific Gallabox department. The agent then assigns the chat to the correct human queue without any manual dispatcher intervention.

Deploy autonomous response agents

The `list_thread_messages` tool feeds the entire conversation history into your CrewAI memory bank. A specialized responder agent analyzes the past ten messages to understand the context. It determines the precise technical answer required. The responder then triggers `send_whatsapp_text` to reply directly to the customer. You configure the agent's role to strictly adhere to your company guidelines. The client receives an instant, accurate answer while your human staff focuses on harder tickets.

Manage templates and broadcasts

The `list_message_templates` tool retrieves all approved Meta templates currently active in your account. A dedicated outbound agent reviews these options. It selects the exact template ID needed for a shipping update or payment reminder. The agent then calls `send_whatsapp_template` to dispatch the notification. Because CrewAI supports sequential execution, you can chain these actions. Agent A verifies the order status, and Agent B fires the WhatsApp template via the MCP standard.

Setup guide

Set up Gallabox 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 Gallabox tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Pass your endpoint URL directly into the agent's `mcps` array. CrewAI automatically connects, fetches the tool schemas, and makes them available to that specific agent.

Yes. You define the server connection once and assign it to multiple agents in your crew. They will share the interface, though you should monitor rate limits if multiple agents send messages simultaneously.

Use `MCPServerHTTP` from the toolkit and apply a `tool_filter`. You can restrict your research agent to read-only tools like `get_chat_details` while only allowing the response agent to use messaging tools.

The tool call returns an error string to the agent. Because CrewAI agents are autonomous, they read the error, wait, and attempt the operation again based on their underlying logic.

The integration operates on a zero-trust architecture. WhatsApp message strings and profile details exist only during the active tool execution. The sandbox terminates immediately, leaving no persistent data behind.

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