How to Use the API Ninjas MCP in Mastra AI

Q: How do I register API Ninjas tools inside a Mastra AI agent using MCP?

Install @mastra/mcp@latest and instantiate MCPClient with your Vinkius URL. Call mcpClient.listTools() and spread the result into your agent's tool configuration array.

Q: How do I require human approval before Mastra AI logs sleep data?

Set requireToolApproval to true on the getsleep tool within your agent setup. This halts execution and prompts you to confirm before the agent processes the sleep metrics.

Q: Can I combine TDEE and BMR tools in a single workflow?

Yes. You can chain getbmr and gettdee sequentially. The output of your BMR calculation can feed directly into your TDEE activity multipliers.

Build self-correcting health and fitness workflows with Mastra AI and the API Ninjas MCP Server.

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Connect API Ninjas MCP to Mastra AI

Create your Vinkius account to connect API Ninjas 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 API Ninjas with Mastra AI

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Build automated fitness workflows in Mastra AI

The `get_exercises` and `get_calories_burned` tools plug directly into your Mastra agent workflows. You can build loops that search for chest exercises and calculate the exact energy cost for a 45-minute session. If the API call fails or times out, Mastra handles it with automatic exponential backoff. Your workflow keeps running, retrying the query without crashing your main application thread.

Track body metrics with conditional logic

The `get_body_fat` and `get_tdee` tools let you build branching logic based on physical changes. For example, if a user's calculated body fat drops below a target percentage, your agent automatically triggers a new TDEE recalculation using this MCP Server. You define these steps in a single TypeScript file. Mastra routes the output of one tool straight into the parameters of the next.

Monitor sleep and steps in production

The `get_sleep` and `get_steps` tools allow your agent to audit daily habits. The model checks if step counts or sleep durations hit recommended healthy baselines. You deploy these monitoring agents to any cloud provider with a single command. The framework manages the transport layers so you focus on writing the logic.

Setup guide

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

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

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

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Common questions about API Ninjas MCP in Mastra AI

Install `@mastra/mcp@latest` and instantiate `MCPClient` with your Vinkius URL. Call `mcpClient.listTools()` and spread the result into your agent's tool configuration array.

Yes. Mastra has a built-in workflow engine that manages retries and backoff. If you hit rate limits while pulling nutrition data, the framework pauses and retries automatically.

Set `requireToolApproval` to true on the `get_sleep` tool within your agent setup. This halts execution and prompts you to confirm before the agent processes the sleep metrics.

Yes. You can chain `get_bmr` and `get_tdee` sequentially. The output of your BMR calculation can feed directly into your TDEE activity multipliers.

All physical activity logs and sleep durations pass through zero-trust, isolated environments. No health databases are retained on our servers, and token-based authentication keeps your endpoint secure.

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