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Using Tools

Agents use tools to call APIs, query databases, or run custom functions from your codebase. Tools give agents capabilities beyond language generation by providing structured access to data and performing clearly defined operations. You can also load tools from remote MCP servers to expand an agent's capabilities.

When to use tools​

Use tools when an agent needs additional context or information from remote resources, or when it needs to run code that performs a specific operation. This includes tasks a model can't reliably handle on its own, such as fetching live data or returning consistent, well defined outputs.

Creating a tool​

When creating tools, keep descriptions simple and focused on what the tool does, emphasizing its primary use case. Descriptive schema names can also help guide the agent on how to use the tool.

This example shows how to create a tool that fetches weather data from an API. When the agent calls the tool, it provides the required input as defined by the tool's inputSchema. The tool accesses this data through its inputData parameter, which in this example includes the location used in the weather API query.

src/mastra/tools/weather-tool.ts
import { createTool } from "@mastra/core/tools";
import { z } from "zod";

export const weatherTool = createTool({
  id: "weather-tool",
  description: "Fetches weather for a location",
  inputSchema: z.object({
    location: z.string(),
  }),
  outputSchema: z.object({
    weather: z.string(),
  }),
  execute: async (inputData) => {
    const { location } = inputData;

    const response = await fetch(`https://wttr.in/${location}?format=3`);
    const weather = await response.text();

    return { weather };
  },
});

Adding tools to an agent​

To make a tool available to an agent, add it to tools. Mentioning available tools and their general purpose in the agent's system prompt helps the agent decide when to call a tool and when not to.

An agent can use multiple tools to handle more complex tasks by delegating specific parts to individual tools. The agent decides which tools to use based on the user's message, the agent's instructions, and the tool descriptions and schemas.

src/mastra/agents/weather-agent.ts
import { Agent } from "@mastra/core/agent";
import { weatherTool } from "../tools/weather-tool";

export const weatherAgent = new Agent({
  id: "weather-agent",
  name: "Weather Agent",
  instructions: `
      You are a helpful weather assistant.
      Use the weatherTool to fetch current weather data.`,
  model: "openai/gpt-5.1",
  tools: { weatherTool },
});

Calling an agent​

The agent uses the tool's inputSchema to infer what data the tool expects. In this case, it extracts London as the location from the message and passes it to the tool's inputData parameter.

src/test-tool.ts
import { mastra } from "./mastra";

const agent = mastra.getAgent("weatherAgent");

const result = await agent.generate("What's the weather in London?");

Using multiple tools​

When multiple tools are available, the agent may choose to use one, several, or none, depending on what's needed to answer the query.

src/mastra/agents/weather-agent.ts
import { weatherTool } from "../tools/weather-tool";
import { activitiesTool } from "../tools/activities-tool";

export const weatherAgent = new Agent({
  id: "weather-agent",
  name: "Weather Agent",
  // ..
  tools: { weatherTool, activitiesTool },
});