# Built-in Tools

## Introduction

Built-in Tools are pre-designed tools that agents can use directly without any editing or customization. These tools come with predefined functionalities and interfaces, enabling quick and stable integration into agent workflows.

* **Predefined Functionality**: Built-in tools have fixed features and behaviors designed by the product team.
* **Ready to Use**: Agents can invoke these tools immediately without extra configuration or setup.
* **No Editing Supported**: Users cannot modify or customize the internal logic or parameters of built-in tools.
* **Reliable and Stable**: Controlled and tested by the product, ensuring consistent performance.

## Serp Google Search

<figure><img src="/files/xQNRWOOpruOfBMROQ2Ok" alt=""><figcaption></figcaption></figure>

### **Overview**

Serp Google Search is a built-in tool that enables agents to access real-time Google search results. It handles proxy management, CAPTCHA solving, and parses rich structured data, providing seamless and accurate search experiences.

### **Key Features**

* Real-time access to the latest search results
* Extraction of rich snippets such as links, addresses, prices
* Automatic CAPTCHA handling for uninterrupted queries

**Use Cases**

* SEO analysis
* News monitoring
* Price tracking
* Market research

## Tavily Search

<figure><img src="/files/gGza81hv7yA0kC1dL7ef" alt=""><figcaption></figcaption></figure>

### **Overview**

Tavily Search is a search engine designed specifically for AI agents, delivering real-time, accurate, and unbiased results. It aggregates information from multiple data sources to help agents quickly find relevant information.

### **Key Features**

* Real-time, up-to-date search results
* Aggregation across diverse data sources
* Optimized for AI agent integration

### **Use Cases**

* Research assistance
* Data aggregation
* Information retrieval

## Wikipedia Search

<figure><img src="/files/w8r2MvdRJzi0QgF5H7ed" alt=""><figcaption></figcaption></figure>

### **Overview**

Wikipedia Search is a built-in tool allowing agents to query Wikipedia entries, retrieve authoritative content, and generate summaries. It supports full article access and content extraction.

### **Key Features**

* Search and retrieve relevant Wikipedia articles
* Generate concise article summaries
* Access full page content for detailed information

### **Use Cases**

* Knowledge retrieval
* Educational applications
* Content summarization

## ECharts Visualization

<figure><img src="/files/8zL5d21oEOGvOdH1O41s" alt=""><figcaption></figcaption></figure>

### **Overview**

ECharts Visualization is a built-in data visualization tool leveraging Apache ECharts. It enables agents to create interactive, customizable charts such as line, bar, and pie charts, supporting large dataset rendering.

### **Key Features**

* Interactive chart types including line, bar, pie, and more
* Highly customizable visual options
* Efficient rendering for big data sets

### **Use Cases**

* Data analysis
* Dashboard creation
* Business intelligence visualization

## HTML Report

<figure><img src="/files/5wNXydVH7yBnEqS2Y2G9" alt=""><figcaption></figcaption></figure>

### **Overview**

HTML Report is a built-in tool that allows agents to generate structured reports in HTML format. It supports customizable templates and easy integration, facilitating report sharing and presentation.

### **Key Features**

* Generate detailed, formatted HTML reports
* Support for various customizable templates
* Seamless integration with other tools and data sources

### **Use Cases**

* Test and performance reporting
* Project documentation
* Data presentation and sharing


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.workfx.ai/workfx-1.1.x-english/workforce-factory/tools/built-in-tools.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.