How to Use the TfL MCP in OpenAI Agents SDK
Build production systems with the OpenAI Agents SDK. Reliable TfL data integration.
Works with every AI agent you already use
…and any MCP-compatible client
Connect TfL MCP to OpenAI Agents SDK
Create your Vinkius account to connect TfL to OpenAI Agents SDK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.
Gemini
Key Capabilities
Plan multi-modal journeys using MCP Server
The `get_journey` tool calculates trips across all London transport modes—tube, bus, train, bike, even walking. It gives you total duration, CO2 savings, and detailed instructions for every leg of the trip. Your agent compares multiple options automatically, helping users plan everything from Paddington to Greenwich while providing fare estimates.
Check service reliability via MCP Server
Need to know if a line is running? The `get_line_status` tool returns the current status for every TfL line, flagging anything from 'Minor Delays' to 'Suspended.' It lets you query all lines or filter by mode. This gives your agent critical context before planning any journey, ensuring users aren't heading out based on old data.
Identify road issues using MCP Server
Before a car trip starts, use `get_road_disruptions` to check for current closures or major incidents. This tool reports the affected segment, the cause (like roadworks), and how severe it is. Your agent can cross-reference this data with planned routes, giving drivers immediate warnings about detours before they even leave the car.
Set up TfL 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 TfL tools at runtime. - 3
Create your Agent
Pass the MCP to
Agent(mcp_servers=[server]). The agent receives TfL tools as native definitions — JSON schemas resolve automatically. - 4
Run the agent
Call
Runner.run(agent, prompt)to execute. The agent invokes the appropriate TfL 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="TfL Agent",
instructions="You have access to TfL 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 TfL. 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.
Why Choose Vinkius
Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.
Real-time monitoring
Live
visibility into every interaction
Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.
Built-in savings
60%
lower AI costs
Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.
Single dashboard
One
place for every integration
Every tool your AI connects to, managed from a single screen. One account, complete control.
Common questions about TfL MCP in OpenAI Agents SDK
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