> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bindai.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# 10.4 tool

# Tool API Reference

The `Tool` API enables agents to interact with the outside world.

While language models excel at reasoning and generating text, tools allow agents to perform real actions such as querying databases, calling APIs, reading files, or executing custom business logic.

This page documents the public Tool API in BindAI.

***

# Overview

A tool is a callable component that extends an agent's capabilities.

Conceptually:

```text id="api-tool-1" theme={null}
Agent

↓

Tool

↓

External System

↓

Result
```

Tools bridge AI reasoning and real-world operations.

***

# Creating a Tool

A tool is typically implemented as a Python function or callable object.

Example:

```python id="api-tool-2" theme={null}
def search(query: str):
    ...
```

Once registered, the tool becomes available to agents.

***

# Registering a Tool

Tools are usually registered at the project level.

Example:

```python id="api-tool-3" theme={null}
project.add_tool(
    search_tool,
)
```

After registration, multiple applications and agents can reuse the same tool.

***

# Tool Registry

Projects maintain a centralized tool registry.

```text id="api-tool-4" theme={null}
Project

↓

Tool Registry

↓

Agents
```

The registry manages discovery and reuse of tools across the project.

***

# Tool Execution

During agent execution, the language model may decide to invoke a tool.

```text id="api-tool-5" theme={null}
User Request

↓

Agent

↓

Tool

↓

Result

↓

Agent Response
```

The tool executes synchronously and returns its result to the agent.

***

# Tool Inputs

Tools receive structured arguments supplied by the agent.

Typical inputs include:

* strings
* numbers
* booleans
* dictionaries
* lists

Input validation should always be performed before executing business logic.

***

# Tool Outputs

A tool returns data back to the agent.

Examples include:

* text
* JSON objects
* structured records
* search results
* API responses

The returned value becomes part of the agent's reasoning process.

***

# Tool Context

Some tools require access to execution context.

Conceptually:

```text id="api-tool-6" theme={null}
Workflow Context

↓

Tool

↓

Updated Context
```

Context-aware tools can read workflow variables or execution metadata when appropriate.

***

# Tool Results

Tool execution may produce either:

```text id="api-tool-7" theme={null}
Success

or

Failure
```

Failures should be reported through structured exceptions or result objects rather than silently ignored.

***

# Shared Tools

A single tool can be shared across multiple applications.

```text id="api-tool-8" theme={null}
Shared Tool

↙       ↓       ↘

Support

Sales

Research
```

Sharing tools avoids duplicate implementations.

***

# Local Tools

Some tools are specific to one application.

Example:

```text id="api-tool-9" theme={null}
Support Application

↓

Ticket Tool
```

These tools do not need to be registered globally if they are not reused elsewhere.

***

# Tool Discovery

Agents only use tools that are explicitly registered or assigned to them.

This improves:

* security
* predictability
* performance
* prompt clarity

Unused tools should not be exposed to every agent.

***

# Error Handling

Tools should gracefully handle:

* invalid input
* unavailable services
* authentication failures
* network errors
* unexpected exceptions

Returning meaningful errors makes workflows easier to debug.

***

# Security

Because tools often interact with external systems, they should:

* validate user input
* sanitize parameters
* limit permissions
* protect secrets
* avoid exposing internal resources

Tool security is critical in production deployments.

***

# Performance

Well-designed tools should:

* minimize network requests
* cache expensive operations where appropriate
* reuse existing connections
* avoid unnecessary computation

Efficient tools improve the responsiveness of every agent that uses them.

***

# Best Practices

* Design each tool for a single responsibility.
* Register reusable tools at the project level.
* Validate all incoming arguments.
* Return structured, predictable results.
* Handle failures gracefully.
* Keep tool interfaces stable.
* Document expected inputs and outputs.

***

# Related APIs

The Tool API integrates with:

* Agent
* Project
* Workflow
* Memory
* Knowledge

Together they provide BindAI's extensibility layer.

***

# Summary

The Tool API extends BindAI agents beyond language generation.

By connecting agents to databases, APIs, files, business systems, and custom logic, tools enable AI applications to perform meaningful real-world work while remaining reusable, secure, and easy to maintain.