How to Use the CallFire MCP in Mastra AI

Q: Where is my CallFire text history processed in Mastra AI?

Text logs retrieved via gettext are processed entirely in memory during workflow execution. Vinkius runs the underlying MCP Server in secure V8 isolates, meaning your message logs are never saved to disk or used for training.

Build resilient CallFire voice and text automation workflows using the Mastra AI agent framework.

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Connect CallFire MCP to Mastra AI

Create your Vinkius account to connect CallFire 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 CallFire with Mastra AI

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Automated Campaign Retries with Mastra AI

This MCP Server exposes campaign tools like `list_campaigns` to monitor active broadcasts and trigger retries if performance metrics drop below your threshold. When high-volume voice campaigns stall, you need automated recovery. Using Mastra's built-in workflow engine, the agent handles transient API errors gracefully. If `get_campaign` fails due to rate limits, the framework automatically backs off and retries the call without crashing your background service.

Conditional Text Notification Workflows

This MCP Server exposes text tools like `list_texts` to scan incoming messages and route the conversation based on the sentiment or keywords found in the message body. Set up complex logic paths using MCP tools based on customer replies. If a customer requests a callback, the workflow triggers `get_text` to verify the sender's identity, then flags the record for manual follow-up. You write the branching logic in TypeScript, and the agent executes the steps reliably.

Human-in-the-Loop Webhook Management

This MCP Server exposes webhook tools like `list_webhooks` to inspect active endpoints. Modifying live communication channels requires oversight, and your agent can list active endpoints but requires explicit developer approval before making any structural changes to your messaging infrastructure. By using Mastra's tool approval flags, you keep control over critical operations. The agent drafts the webhook updates after running `get_webhook`, then pauses for your confirmation in the console before applying the changes.

Setup guide

Set up CallFire 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 CallFire 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: "callfire-alternative-mcp-client",
  servers: {
    "callfire-alternative-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent CallFire 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 CallFire. 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.

Why Choose Vinkius

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Common questions about CallFire MCP in Mastra AI

Initialize the MCP client with your Vinkius HTTP endpoint and call `mcpClient.listTools()`. You then spread those tools directly into your agent's tool configuration array. This gives your agent immediate access to operations like `list_calls` and `get_contact`.

Yes. Mastra's workflow engine excels at handling API rate limits. When your agent calls `list_contacts` or `get_call`, any temporary connection drops or limit blocks trigger automatic retries with exponential backoff.

You can register a workflow step that periodically checks your integrations. The agent runs `list_webhooks` to fetch active listeners, parses the metadata via `get_webhook`, and triggers subsequent workflow steps based on those configurations.

Yes. You can write a preprocessing step in your workflow that calls `list_contacts` but only feeds a specific subset of phone numbers to the agent. This prevents your context window from getting cluttered with irrelevant data.

Text logs retrieved via `get_text` are processed entirely in memory during workflow execution. Vinkius runs the underlying MCP Server in secure V8 isolates, meaning your message logs are never saved to disk or used for training.

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