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.

Quickstart

Import createTool from @mastra/core/tools and define a tool with an id, description, inputSchema, outputSchema, and execute function.

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 }
  },
})

When creating tools, keep descriptions concise 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.

note

Visit the createTool reference for more information on available properties, configurations, and examples.

To make a tool available to an agent, add it to the tools property on the Agent class. 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.

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-5.4',
  tools: { weatherTool },
})

Multiple tools

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'
import { hazardsTool } from '../tools/hazards-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.
    Use the hazardsTool to provide information about potential weather hazards.`,
  model: 'openai/gpt-5.4',
  tools: { weatherTool, hazardsTool },
})

Agents as tools

Add subagents through the agents configuration to create a supervisor. Mastra converts each subagent to a tool named agent-<key>. Include a description on each subagent so the supervisor knows when to delegate.

src/mastra/agents/supervisor.ts

import { Agent } from '@mastra/core/agent'

const writer = new Agent({
  id: 'writer',
  name: 'Writer',
  description: 'Drafts and edits written content',
  instructions: 'You are a skilled writer.',
  model: 'openai/gpt-5.4',
})

export const supervisor = new Agent({
  id: 'supervisor',
  name: 'Supervisor',
  instructions: 'Coordinate the writer to produce content.',
  model: 'openai/gpt-5.4',
  agents: { writer },
})

Workflows as tools

Add workflows through the workflows configuration. Mastra converts each workflow to a tool named workflow-<key>, using the workflow's inputSchema and outputSchema. Include a description on the workflow so the agent knows when to trigger it.

src/mastra/agents/research-agent.ts

import { Agent } from '@mastra/core/agent'
import { researchWorkflow } from '../workflows/research-workflow'

export const researchAgent = new Agent({
  id: 'research-agent',
  name: 'Research Agent',
  instructions: 'You are a research assistant.',
  model: 'openai/gpt-5.4',
  workflows: { researchWorkflow },
})

Shape output for the model

Use toModelOutput when your tool returns rich structured data for your application, but you want the model to receive a smaller or multimodal representation. This keeps model context focused while preserving the full tool result in your app.

src/mastra/tools/weather-tool.ts

export const weatherTool = createTool({
  execute: async ({ location }) => {
    const response = await fetch(`https://wttr.in/${location}?format=j1`)
    const data = await response.json()

    return {
      location,
      temperature: data.current_condition[0].temp_F,
      condition: data.current_condition[0].weatherDesc[0].value,
      weatherIconUrl: data.current_condition[0].weatherIconUrl[0].value,
      source: data,
    }
  },
  toModelOutput: output => {
    return {
      type: 'content',
      value: [
        {
          type: 'text',
          text: `${output.location}: ${output.temperature}F and ${output.condition}`,
        },
        { type: 'image-url', url: output.weatherIconUrl },
      ],
    }
  },
})

Control tool selection

Pass toolChoice or activeTools to .generate() or .stream() to control which tools the agent uses at runtime.

await agent.generate('Check the forecast', {
  toolChoice: 'required',
  activeTools: ['weatherTool'],
})

note

See the Agent.generate() reference for all runtime options including toolsets, clientTools, and prepareStep.

Control toolName in stream responses

The toolName in stream responses is determined by the object key you use, not the id property of the tool, agent, or workflow.

export const weatherTool = createTool({
  id: 'weather-tool',
})

// Using the variable name as the key
tools: { weatherTool }
// Stream returns: toolName: "weatherTool"

// Using the tool's id as the key
tools: { [weatherTool.id]: weatherTool }
// Stream returns: toolName: "weather-tool"

// Using a custom key
tools: { "my-custom-name": weatherTool }
// Stream returns: toolName: "my-custom-name"

This lets you specify how tools are identified in the stream. If you want the toolName to match the tool's id, use the tool's id as the object key.

Subagents and workflows as tools

Subagents and workflows follow the same pattern. They're converted to tools with a prefix followed by your object key:

Property	Prefix	Example key	toolName
agents	agent-	weather	agent-weather
workflows	workflow-	research	workflow-research

const orchestrator = new Agent({
  agents: {
    weather: weatherAgent, // toolName: "agent-weather"
  },
  workflows: {
    research: researchWorkflow, // toolName: "workflow-research"
  },
})

Note that for subagents, you'll see two different identifiers in stream responses:

• toolName: "agent-weather" in tool call events — the generated tool wrapper name
• id: "weather-agent" in data-tool-agent chunks — the subagent's actual id property

Related

• createTool reference
• Agent.generate() reference: Runtime options for tool selection, steps, and callbacks
• MCP overview
• Dynamic tool search: Load tools on demand for agents with large tool libraries
• Tools with structured output: Model compatibility when combining tools and structured output
• Agent approval
• Request context