TripGo MCP Server for CrewAI 9 tools — connect in under 2 minutes
Connect your CrewAI agents to TripGo through Vinkius, pass the Edge URL in the `mcps` parameter and every TripGo tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
from crewai import Agent, Task, Crew
agent = Agent(
role="TripGo Specialist",
goal="Help users interact with TripGo effectively",
backstory=(
"You are an expert at leveraging TripGo 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 TripGo "
"and summarize their capabilities."
),
agent=agent,
expected_output=(
"A detailed summary of 9 available tools "
"and what they can do."
),
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)
* 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 TripGo MCP Server
What you can do
Connect AI agents to the TripGo platform for intelligent multimodal journey planning:
When paired with CrewAI, TripGo becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call TripGo tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
- Plan trips combining bus, train, subway, tram, ferry, walking, and cycling
- Find nearby transit stops by GPS coordinates with distance and route info
- Search stops by name or address for precise location discovery
- Get real-time departures and arrivals with live delay estimates
- Track vehicle positions on the map with real-time GPS data
- Review route information including all stops and agency details
- Check stop details with accessibility and amenity information
- Access global regions covering major cities worldwide
The TripGo MCP Server exposes 9 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.
How to Connect TripGo to CrewAI via MCP
Follow these steps to integrate the TripGo MCP Server with CrewAI.
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 9 tools from TripGo
Why Use CrewAI with the TripGo MCP Server
CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with TripGo 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
TripGo + CrewAI Use Cases
Practical scenarios where CrewAI combined with the TripGo MCP Server delivers measurable value.
Automated multi-step research: a reconnaissance agent queries TripGo 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 TripGo, analyzes trends over time, and generates executive briefings in markdown or PDF format
Multi-source enrichment pipelines: chain TripGo 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 TripGo against predefined policy rules, generates deviation reports, and routes findings to the appropriate team
TripGo MCP Tools for CrewAI (9)
These 9 tools become available when you connect TripGo to CrewAI via MCP:
get_arrivals
Returns route names, origins, scheduled vs estimated arrival times, and delays. Use this to track incoming vehicles. Requires stop ID. Get upcoming arrivals to a transit stop
get_departures
Returns route names, destinations, scheduled vs estimated departure times, and delays. Use this to check when your next ride arrives. Requires stop ID. Get upcoming departures from a transit stop
get_nearby_stops
Returns stop IDs, names, coordinates, routes serving each stop, and distance from search point. Use this to find nearest transit options before planning trips. Find transit stops near a GPS coordinate
get_regions
Each region has an ID, name, and coverage area. Use this first to verify your city is covered before planning trips. Supports major cities across North America, Europe, Australia, and Asia. List all available transit regions supported by TripGo
get_route_info
Requires route ID. Use this to understand route coverage before planning trips. Get information about a specific transit route
get_stop_details
Requires stop ID from nearby stops or search results. Use this to review stop facilities before waiting there. Get detailed information about a specific transit stop
get_vehicle_positions
Optionally filter by route ID. Use this for real-time tracking of vehicles on the map. Get real-time vehicle positions for transit vehicles
plan_trip
Combines public transport (bus, train, subway, tram, ferry) with walking and cycling. Returns multiple trip options with departure/arrival times, duration, number of transfers, and step-by-step instructions. Optionally specify travel time and preferred transport modes. Plan a multimodal trip between two coordinates
search_stops
g., "Times Square", "Main St & 5th Ave"). Returns matching stops with IDs, names, coordinates, routes, and relevance scores. Use this when you know the stop name or intersection but not exact coordinates. Search for transit stops by name or address
Example Prompts for TripGo in CrewAI
Ready-to-use prompts you can give your CrewAI agent to start working with TripGo immediately.
"Plan a trip from Central Station to Opera House using only public transit and walking"
"What buses are departing from Stop 12345 in the next 15 minutes?"
"Show me all train and bus vehicles currently running on Route 480"
Troubleshooting TripGo MCP Server with CrewAI
Common issues when connecting TripGo to CrewAI through the Vinkius, and how to resolve them.
MCP tools not discovered
Agent not using tools
Timeout errors
Rate limiting or 429 errors
TripGo + CrewAI FAQ
Common questions about integrating TripGo MCP Server with CrewAI.
How does CrewAI discover and connect to MCP tools?
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?
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?
Can CrewAI agents call multiple MCP tools in parallel?
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)?
crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.Connect TripGo with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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GitHub Copilot in VS Code with Agent mode and MCP support.
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Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect TripGo to CrewAI
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.