How to Use the SnapCall MCP in Mastra AI

Q: How do I delete old SnapCall call streams using @mastra/mcp?

You can use archivecallstream within your workflow. This gives the agent explicit control over deleting or archiving specific, completed call data.

Build resilient workflows handling failures with SnapCall integration using @mastra/mcp.

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Connect SnapCall MCP to Mastra AI

Create your Vinkius account to connect SnapCall to Mastra AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup SnapCall with Mastra AI

Ask AI about this MCP

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Claude

Perplexity

Multi-Step Workflow Execution with MCP Server

Your agent can handle complex, multi-stage tasks. For instance, you'll use `get_ai_insights_for_call` to pull data, then pass that result into another step in your workflow engine. This ensures the entire process completes even if one part fails.

Automated Event Triggering

Use `create_webhook` to set up reliable triggers for business events like a successful call or a new clip request. The framework handles automatic retries and exponential backoff, so your complex processes don't just crash when the API is slow.

Core Call Management

The `create_call` tool lets your agent initiate meetings programmatically. You can also manage existing data by calling `get_call_details`, giving you a full picture of any given call stream at the time of execution.

Setup guide

Set up SnapCall MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All SnapCall tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "snapcall-mcp-client",
  servers: {
    "snapcall-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "SnapCall Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to SnapCall tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent SnapCall transactions"
);
console.log(result.text);

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by SnapCall. 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.

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Common questions about SnapCall MCP in Mastra AI

The platform handles automatic retries and failure logic, which is key. If a service call fails mid-workflow, the agent doesn't quit; it tries again with different parameters.

Yes. You can set up webhooks to trigger scheduled tasks that call tools like `get_ai_insights_for_call`. This ensures the insights are always available when your main workflow runs.

The agent framework is designed for complex resource handling. You can manage different calls and webhooks across various scopes, keeping your operations contained but scalable.

It primarily works with call records and webhook configurations. Since these are critical business process assets, robust error handling is necessary to ensure data integrity.

You can use `archive_call_stream` within your workflow. This gives the agent explicit control over deleting or archiving specific, completed call data.

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