Bring Omnichannel Inbox
to OpenAI Agents SDK
Learn how to connect Trengo to OpenAI Agents SDK and start using 12 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
What is the 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.
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.
How it works
1. Subscribe to this server
2. Enter your Trengo API Token (found in your account profile or settings)
3. Start managing your unified inbox from Claude, Cursor, or any MCP-compatible client
Who is this for?
- Support Managers — quickly retrieve ticket histories and monitor team activity via simple AI commands.
- Customer Success Teams — respond to inquiries and update ticket statuses directly from the workspace.
- Operations Leads — coordinate communication channels and monitor webhook events via the AI assistant.
Built-in capabilities (12)
Create a new ticket
Create a new webhook
Get current user profile
Get ticket details
). List communication channels
List all contacts
List ticket messages
List team users
List all support tickets
List configured webhooks
Send a message
Update ticket status
Why OpenAI Agents SDK?
The OpenAI Agents SDK auto-discovers all 12 tools from Trengo through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Trengo, another analyzes results, and a third generates reports, all orchestrated through Vinkius.
- —
Native MCP integration via
MCPServerSse, pass the URL and the SDK auto-discovers all tools with full type safety - —
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
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Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
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First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
Trengo in OpenAI Agents SDK
Trengo and 3,400+ other MCP servers. One platform. One governance layer.
Teams that connect Trengo to OpenAI Agents SDK through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 3,400+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Trengo in OpenAI Agents SDK
The Trengo 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. All 12 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in OpenAI Agents SDK only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
Trengo for OpenAI Agents SDK
Every tool call from OpenAI Agents SDK to the Trengo MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
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.
How does the OpenAI Agents SDK connect to MCP?
Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.
Can I use multiple MCP servers in one agent?
Yes. Pass a list of MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.
Does the SDK support streaming responses?
Yes. The SDK supports SSE and Streamable HTTP transports, both of which work natively with Vinkius.
MCPServerStreamableHttp not found
Ensure you have the latest version: pip install --upgrade openai-agents
Agent not calling tools
Make sure your prompt explicitly references the task the tools can help with.