How to Use the ChatBot.com MCP in Mastra AI

Q: How do I initialize the ChatBot.com tools in Mastra AI?

Run npm install @mastra/mcp@latest. Create a new MCPClient with your Vinkius URL, then spread mcpClient.listTools() into your Mastra agent configuration.

Q: Can Mastra AI require approval before changing ChatBot.com data?

This specific MCP Server only exposes read-only tools like listchatbotstories. If you add mutation tools later, Mastra's requireToolApproval flag handles human-in-the-loop validation.

Automate bot maintenance with conditional workflows. Link ChatBot.com to Mastra AI to handle failed interactions and retrain intents.

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Connect ChatBot.com MCP to Mastra AI

Create your Vinkius account to connect ChatBot.com 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 ChatBot.com with Mastra AI

Ask AI about this MCP

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Claude

Perplexity

Branching Workflows for ChatBot.com

Extracting unrecognized user phrases relies on the `list_training_data` tool. Mastra AI takes this raw list of failed matches and feeds it into a conditional pipeline. If the list contains more than fifty items, the workflow branches to notify a human administrator via Slack. Handling temporary API failures is built right into the framework. When fetching data from the MCP Server, Mastra automatically applies exponential backoff. Your retraining pipeline won't crash just because of a momentary timeout.

Multi-Step Interaction Audits

Auditing entire conversation paths starts with `list_chatbot_stories` followed by `list_story_interactions`. Your Mastra agent retrieves the top-level workflows, then loops through each one to gather specific user messages. This multi-step execution runs reliably in the background. Complex data gathering needs solid error handling. If a specific story ID fails to load, the workflow engine catches the error and proceeds to the next one. You get a complete audit log without writing custom retry logic.

Sync Entities via MCP Server

Grabbing custom NLP matching rules requires the `list_chatbot_entities` tool. You can combine this with `list_chatbot_users` to build a daily sync job. The agent pulls the latest entities and user profiles, then formats them for your data warehouse. Deploying these sync jobs takes one command. You can push the Mastra AI workflow to any cloud provider. The Vinkius endpoint handles authentication, so your deployed agent just needs its single access token.

Setup guide

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

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

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

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Common questions about ChatBot.com MCP in Mastra AI

Run `npm install @mastra/mcp@latest`. Create a new `MCPClient` with your Vinkius URL, then spread `mcpClient.listTools()` into your Mastra agent configuration.

This specific MCP Server only exposes read-only tools like `list_chatbot_stories`. If you add mutation tools later, Mastra's `requireToolApproval` flag handles human-in-the-loop validation.

Yes. Any network hiccups between Mastra AI and ChatBot.com trigger automatic exponential backoff.

The framework auto-detects the best available transport. It defaults to Streamable HTTP or SSE based on the Vinkius endpoint configuration.

Ephemeral execution guarantees privacy. When your agent pulls demographic info via `get_chatbot_user_details`, the data passes through a zero-trust proxy. No request headers or payload contents are logged permanently.

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