Can my AI automatically reroute an assigned driver?

Yes. If an urgent delivery arises or a driver is delayed, you can prompt your agent to find another available fleet driver and assign them to the task. It bypasses the engine's built-in optimization logic and enforces the manual assignment on the Bringg dashboard globally.

Can I fetch the exact live location for a specific order?

Yes! Use the `get_task_timeline` tool. Your AI agent can query a specific task ID, reading the timeline progression as well as the active geolocation coordinates. This is perfect for support agents dealing with "Where is my order?" queries who don't want to open a secondary tab.

What happens if a driver forgets to mark a task complete on their app?

You can force the status progression using your AI agent. Tools like `force_task_complete` and `force_task_start` allow you to manually push the task into its finalized state inside your chat, instantly solving tracking desyncs for your logistics operation.

Bringg MCP Server for OpenAI Agents SDK 10 tools — connect in under 2 minutes

Name: Bringg
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 10 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Bringg through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Bringg Assistant",
            instructions=(
                "You help users interact with Bringg. "
                "You have access to 10 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Bringg"
        )
        print(result.final_output)

asyncio.run(main())

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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 Bringg MCP Server

Connect your Bringg account to any AI agent and take full control of your final-mile delivery and dispatch operations through natural conversation.

The OpenAI Agents SDK auto-discovers all 10 tools from Bringg through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Bringg, another analyzes results, and a third generates reports, all orchestrated through Vinkius.

What you can do

Delivery Tasks — Create, update, list, and cancel delivery tasks dynamically before the truck leaves your hub
Fleet Dispatch — Manually assign specific drivers to tasks, bypassing default optimization algorithms
Live Timelines — Pull real-time geolocated tracking data and status estimates for any active order
Force Progression — Manually trigger task start or completion states to keep the dispatch board accurate
Driver CRM — List all human drivers across the fleet, track their availability, and analyze active limits
Customer Database — Instantly retrieve historical data for past delivery recipients

The Bringg MCP Server exposes 10 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Bringg to OpenAI Agents SDK via MCP

Follow these steps to integrate the Bringg MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

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

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 10 tools from Bringg

Why Use OpenAI Agents SDK with the Bringg MCP Server

OpenAI Agents SDK provides unique advantages when paired with Bringg through the Model Context Protocol.

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

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Bringg + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Bringg MCP Server delivers measurable value.

Automated workflows: build agents that query Bringg, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries Bringg, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Bringg tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Bringg to resolve tickets, look up records, and update statuses without human intervention

Bringg MCP Tools for OpenAI Agents SDK (10)

These 10 tools become available when you connect Bringg to OpenAI Agents SDK via MCP:

assign_driver_to_task

Manually override optimization and assign a specific driver to a task

cancel_task_dispatch

Cancel and permanently remove a delivery task from the dispatch schedule

create_delivery_task

Create a new delivery task (order) in the Bringg Delivery Hub

force_task_complete

Force a delivery task status to COMPLETE (successfully delivered)

force_task_start

Force a delivery task status to START (driver en route)

get_task_timeline

Retrieve comprehensive details and live timeline for a specific task

list_active_tasks

` mapping the SaaS dashboard directly isolating pending deliveries. Retrieve a paginated list of active delivery tasks/orders

list_customer_crm

List historical delivery recipients (customers) registered in Bringg

list_fleet_drivers

List all human drivers (users) within the Bringg fleet network

update_task_details

Modify existing delivery task details such as customer notes or dropoff info

Example Prompts for Bringg in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Bringg immediately.

"Show me the top 3 most recent active deliveries in the hub."

"Where is the order for Task ID 3109 and what's its exact timeline?"

"Force mark task 9481 as complete, the driver forgot to do it."

Troubleshooting Bringg MCP Server with OpenAI Agents SDK

Common issues when connecting Bringg to OpenAI Agents SDK through the Vinkius, and how to resolve them.

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.

Bringg + OpenAI Agents SDK FAQ

Common questions about integrating Bringg MCP Server with OpenAI Agents SDK.

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.