How to Use the Ninox MCP in Mastra AI

Q: Can Mastra AI handle conditional branching based on Ninox query results?

Yes, Mastra AI uses the output of listrecords to route your workflow down different Ninox execution paths. For example, if a Ninox record status is marked as pending, the Mastra AI agent branches to trigger an escalation script.

Q: What is the best way to execute complex formulas in Ninox using Mastra AI?

You should use executeninoxscript within your Mastra AI workflow step to run native Ninox calculations. This offloads heavy logical processing to the Ninox database engine rather than handling it in your Mastra AI TypeScript environment.

Build resilient, multi-step workflows that read, write, and update Ninox database records using Mastra AI agents.

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Setup Ninox with Mastra AI

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Automated Record Syncing with the MCP Server

`list_records` retrieves raw datasets from your Ninox tables to feed directly into Mastra AI workflow steps. Your Mastra AI agent evaluates the returned Ninox data to determine whether to trigger downstream actions.

Transactional Write Pipelines in Mastra AI

`create_records` inserts structured data into your Ninox tables as part of a multi-step Mastra AI pipeline. The Mastra AI framework validates the input schema before making the call, preventing bad data from hitting your Ninox database.

Dynamic Schema Discovery for Workflows

`get_database_schema` inspects your Ninox table relationships so Mastra AI can map incoming payloads dynamically. Your Mastra AI workflows adapt to Ninox database changes without requiring manual field mapping updates.

Setup guide

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

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

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

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Common questions about Ninox MCP in Mastra AI

You instantiate the MCP client and pass the Ninox tools returned by `listTools` directly into the Mastra AI agent configuration. The Mastra AI framework automatically parses the schemas, making operations like `update_record` immediately available.

Yes, Mastra AI uses the output of `list_records` to route your workflow down different Ninox execution paths. For example, if a Ninox record status is marked as pending, the Mastra AI agent branches to trigger an escalation script.

You should use `execute_ninox_script` within your Mastra AI workflow step to run native Ninox calculations. This offloads heavy logical processing to the Ninox database engine rather than handling it in your Mastra AI TypeScript environment.

The Mastra AI workflow engine features built-in exponential backoff for all MCP calls to Ninox. If the Ninox API returns a rate-limit warning during an update, Mastra AI pauses and retries the operation automatically.

Yes, your Ninox database schemas and record payloads are transmitted over encrypted TLS connections directly to your self-hosted Mastra AI runtime. No Ninox database credentials or table structures are stored in external Mastra AI logs, keeping your corporate data isolated.

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