How to Use the Dashdoc MCP in OpenAI Agents SDK
Build production logistics agents with OpenAI Agents SDK that manage Dashdoc fleets and transport orders with built-in execution guardrails.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Dashdoc MCP to OpenAI Agents SDK
Create your Vinkius account to connect Dashdoc to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.
OpenAI Agents SDK for Dashdoc MCP Server
The `list_transports` and `get_transport_details` tools let your OpenAI agent read live delivery statuses, pickup references, and cargo items. You can set up an agent network where one node monitors active loads while another handles customer inquiries. Passing these tools to your agent gives it direct access to logistics data. The SDK's built-in guardrails ensure the agent validates transport IDs before attempting to fetch historical status logs.
Fleet & Driver Operations
Your agent pulls driver profiles and vehicle assignments using the `list_fleet_drivers`, `list_fleet_trucks`, and `list_fleet_trailers` tools. This connects professional names and license plates directly to your OpenAI tracing dashboard. Handoffs become incredibly powerful here. A specialized fleet agent can verify a refrigerated trailer's load capacity and then immediately transfer control to a dispatch agent to assign the route.
Site & Address Resolution
Agents locate specific delivery bays and gate codes by calling `list_saved_addresses` and `search_transports_by_reference`. If a driver reports a missing address, the agent can generate a new one using `create_new_address`. You get full visibility into every site modification. The OpenAI dashboard logs the exact system ID returned when your agent persists new postal codes and city metadata.
Set up Dashdoc MCP in OpenAI Agents SDK
Prerequisites
- Python 3.10+ installed
-
openai-agentspackage (pip install openai-agents) - Active Vinkius subscription with a valid endpoint token
- 1
Install the SDK
Run
pip install openai-agentsto install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed. - 2
Connect via SSE transport
Use
MCPServerSsewith your Vinkius endpoint URL. Replace[YOUR_TOKEN_HERE]with your token from cloud.vinkius.com. The SDK auto-discovers all Dashdoc tools at runtime. - 3
Create your Agent
Pass the MCP to
Agent(mcp_servers=[server]). The agent receives Dashdoc tools as native definitions — JSON schemas resolve automatically. - 4
Run the agent
Call
Runner.run(agent, prompt)to execute. The agent invokes the appropriate Dashdoc tools and returns structured results. Copy the full example on the right to get started.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse
async def main():
async with MCPServerSse(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as server:
agent = Agent(
name="Dashdoc Agent",
instructions="You have access to Dashdoc tools.",
mcp_servers=[server],
)
result = await Runner.run(agent, "List recent transactions")
print(result.final_output)
asyncio.run(main()) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Dashdoc. 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 Dashdoc MCP in OpenAI Agents SDK
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