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Operate WhatsApp through Visto Azul API using natural language commands.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "vistoazul-mcp" yet — see the docs or source repo.
Using Visto Azul's WhatsApp interface, send a promotional message to customers in my contact list: 20% off this weekend, and attach the campaign poster image.
A WhatsApp marketing message with text and image is sent to the selected contacts, with an execution result returned.
Create a PIX charge for a customer for 199 BRL, with the note 'Order #4821', and send it to them via WhatsApp.
The tool creates the PIX payment request and sends it to the target customer over WhatsApp.
Configure a webhook for my WhatsApp account: when a new message arrives, send callbacks to https://example.com/webhook.
A webhook is configured for WhatsApp events, and the tool returns whether setup succeeded.
Support, sales, or operations teams can send text, media, or payment-related messages in natural language without switching between multiple dashboards. It fits routine customer follow-ups and transaction notifications.
Marketers can use the contact and campaign management capabilities to organize WhatsApp outreach and promotional sends. It is suitable for promotions or bulk customer notifications.
Developers can configure webhooks to connect WhatsApp events into their own system workflows. This is useful when message events need to trigger backend business logic.
It is an MCP server that lets AI agents operate WhatsApp through the Visto Azul API. Known capabilities include sending text and media, creating PIX charges, managing contacts and campaigns, and configuring webhooks.
Based on the description, it depends on the Visto Azul API to operate WhatsApp. More specific installation steps, credential setup, and runtime requirements are not provided in the given materials; see the source repository.
It does more than send messages: it also supports media, PIX charges, contact and campaign management, and webhook configuration. These actions can be triggered by AI agents using natural language.
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