Bring Public Transit
to CrewAI
Learn how to connect SPTrans Olho Vivo to CrewAI and start using 13 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the SPTrans Olho Vivo MCP Server?
Connect to the SPTrans Olho Vivo API to bring real-time urban mobility intelligence to your AI agent. Monitor the entire São Paulo bus fleet and provide precise transit information through natural conversation.
What you can do
- Line & Stop Discovery — Search for bus lines by name or number and find specific stops by address or corridor.
- Real-time GPS Tracking — Fetch the exact coordinates of active buses on any given line or across the entire city fleet.
- Arrival Forecasts — Get accurate predictions for when the next bus will arrive at a specific stop or for all stops along a route.
- Corridor & Company Info — List intelligent bus corridors and operating companies to understand the city's transit infrastructure.
- Garage Status — Monitor vehicles currently in the garage for specific companies and lines.
How it works
- Subscribe to this server
- Enter your SPTrans Olho Vivo API Token
- Start querying São Paulo's transit system from Claude, Cursor, or any MCP-compatible client
Who is this for?
- Commuters & Residents — Get instant updates on bus arrivals and locations without switching between multiple transit apps.
- Urban Planners & Researchers — Analyze real-time fleet distribution and corridor efficiency directly through AI-driven data extraction.
- Developers — Integrate São Paulo's transit data into workflows or tools using simple natural language commands.
Built-in capabilities (13)
Get real-time GPS positions for all active buses
Get arrival forecast for a specific stop and line
Get arrival forecast for all stops on a specific line
Get arrival forecast for all lines arriving at a specific stop
Get real-time GPS positions for buses on a specific line
Get real-time GPS positions for buses currently in the garage
List bus operating companies by area
List all intelligent bus corridors in São Paulo
Search for bus lines by number or name
Search for bus lines filtered by direction
Search for bus stops by name or address
Get all stops in a specific intelligent corridor
Get all stops for a specific bus line
Why CrewAI?
When paired with CrewAI, SPTrans Olho Vivo becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call SPTrans Olho Vivo tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
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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
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CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the
mcpsparameter 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
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Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports
SPTrans Olho Vivo in CrewAI
SPTrans Olho Vivo and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect SPTrans Olho Vivo to CrewAI 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 | 4,000+ 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 SPTrans Olho Vivo in CrewAI
The SPTrans Olho Vivo 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 13 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in CrewAI 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
SPTrans Olho Vivo for CrewAI
Every tool call from CrewAI to the SPTrans Olho Vivo MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
How do I find the specific code for a bus line?
Use the search_lines tool with the line number or name (e.g., '8000' or 'Lapa'). The agent will return the line code (cl), which is required for tracking positions or forecasts.
Can I see the arrival times for all buses at a particular stop?
Yes! Use the get_forecast_by_stop tool with the stop code (cp). It will list all upcoming bus arrivals for that location in real-time.
Is it possible to track the live location of buses on a map?
While the MCP returns raw coordinates, you can use get_positions_by_line to get the latitude and longitude of every active bus on a line, which your AI can then describe or plot.
How does CrewAI discover and connect to MCP tools?
CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard 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?
Yes. Each agent has its own 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?
CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.
Can CrewAI agents call multiple MCP tools in parallel?
CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using 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)?
Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.
MCP tools not discovered
Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.
Agent not using tools
Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".
Timeout errors
CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.
Rate limiting or 429 errors
Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.
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