Can I see all the messages in a specific ticket via AI?

Yes! Use the `list_messages` tool and provide the Ticket ID. Your agent will retrieve the full conversation history, including both customer messages and internal notes.

How do I add an internal note to a ticket that the customer won't see?

Use the `send_message` action. Provide the Ticket ID and your text, and set the `internal` parameter to 'true'. This will log the message as a private note for your team.

Is it possible to list all communication channels like WhatsApp and Email?

Absolutely. Use the `list_channels` query. The agent will retrieve all your active integrations, helping you identify which Channel ID to use when creating new tickets.

INSTANT PLUG AND PLAY

Trengo MCP Server for CrewAIGive CrewAI instant access to 12 tools to Create Ticket, Create Webhook, Get Account Profile, and more

Name: Trengo
Availability: InStock
Author: Vinkius

Built by Vinkius GDPR 12 Tools Framework

Connect your CrewAI agents to Trengo through Vinkius, pass the Edge URL in the `mcps` parameter and every Trengo tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Use with Your AI Agent

Ask AI about this App Connector for CrewAI

Open in ChatGPT Open in Claude Open in Perplexity

The Trengo app connector for CrewAI is a standout in the Communication Messaging category — giving your AI agent 12 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Trengo Specialist",
    goal="Help users interact with Trengo effectively",
    backstory=(
        "You are an expert at leveraging Trengo tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Trengo "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 12 available tools "
        "and what they can do."
    ),
)

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

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Trengo MCP Server

Connect your Trengo omnichannel inbox to any AI agent and simplify how you manage customer conversations, team collaboration, and support tickets through natural conversation.

When paired with CrewAI, Trengo becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Trengo tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Unified Inbox Management — List all tickets and conversations across WhatsApp, Email, and Chat in one place.
Ticket Control — Create new support tickets, update statuses (OPEN, CLOSED, ASSIGNED), and manage assignments via AI.
Omichannel Messaging — Send messages to customers or add internal team notes to any conversation.
Contact & Channel Directory — List your customer database and verify all configured communication channels.
Team Coordination — Query team member lists to understand availability and workload.
Event Monitoring — List and create webhooks to track conversation events in real-time.

The Trengo MCP Server exposes 12 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 12 Trengo tools available for CrewAI

When CrewAI connects to Trengo through Vinkius, your AI agent gets direct access to every tool listed below — spanning omnichannel-inbox, helpdesk-ticketing, shared-inbox, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

create_ticket

Create a new ticket

create_webhook

Create a new webhook

get_account_profile

Get current user profile

get_ticket

Get ticket details

list_channels

). List communication channels

list_contacts

List all contacts

list_messages

List ticket messages

list_team_members

List team users

list_tickets

List all support tickets

list_webhooks

List configured webhooks

send_message

Send a message

update_ticket

Update ticket status

Connect Trengo to CrewAI via MCP

Follow these steps to wire Trengo into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 12 tools from Trengo

Why Use CrewAI with the Trengo MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Trengo through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Trengo + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Trengo MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Trengo for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Trengo, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Trengo tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Trengo against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Example Prompts for Trengo in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Trengo immediately.

"List all currently open support tickets."

"Show me the last 3 messages for ticket #88231."

"Close ticket #10293 as 'CLOSED' and add a note 'Resolved via AI'."

Troubleshooting Trengo MCP Server with CrewAI

Common issues when connecting Trengo to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Trengo + CrewAI FAQ

Common questions about integrating Trengo MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.