Compatible with every major AI agent and IDE
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 Google ADK?
Google ADK natively supports SPTrans Olho Vivo as an MCP tool provider. declare Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 13 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.
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Google ADK natively supports MCP tool servers. declare a tool provider and the framework handles discovery, validation, and execution
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Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with SPTrans Olho Vivo
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Production-ready features like session management, evaluation, and deployment come built-in. not bolted on
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Seamless integration with Google Cloud services means you can combine SPTrans Olho Vivo tools with BigQuery, Vertex AI, and Cloud Functions
SPTrans Olho Vivo in Google ADK
SPTrans Olho Vivo and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect SPTrans Olho Vivo to Google ADK 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 Google ADK
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 Google ADK 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 Google ADK
Every tool call from Google ADK 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 Google ADK connect to MCP servers?
Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.
Can ADK agents use multiple MCP servers?
Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.
Which Gemini models work best with MCP tools?
Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.
McpToolset not found
Update: pip install --upgrade google-adk
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