🔧 Agent Tool Protocol (ATP): Empowering LLMs with Real-World Capabilities Using ToolKitClient

Large Language Models (LLMs) such as GPT-4, Claude, and Llama have revolutionized natural language understanding and generation. They can write essays, answer questions, and even create code. However, despite their impressive language abilities, these models are inherently passive — they generate text based on patterns learned during training but cannot directly interact with external systems or perform actions in the real world.

To unlock the true potential of LLMs, we need to empower them with capabilities — the ability to call APIs, run functions, query databases, or control software. This transforms them from static text generators into agentic systems that can reason, plan, and act autonomously.

What is Agent Tool Protocol (ATP)?

Agent Tool Protocol (ATP) is an open protocol and SDK designed to bridge the gap between LLMs and external tools. It provides a standardized framework that allows AI agents powered by LLMs to:

• Discover and understand available tools
• Authenticate securely with third-party services (e.g., via OAuth2)
• Invoke tools remotely in real-time
• Receive and process responses dynamically

By using ATP, developers can expose their own Python functions as remote tools that any ATP-compatible agent can call. This enables LLMs to extend their reasoning with real-world actions, such as creating CRM records, sending emails, or fetching live data.

Why Does ATP Matter?

While LLMs excel at language tasks, many applications require actionable intelligence — the ability to perform tasks beyond text generation. Examples include:

• Automating customer support workflows
• Managing business data in SaaS platforms like HubSpot or Salesforce
• Integrating with IoT devices or robotics
• Building no-code AI assistants that interact with multiple APIs

ATP provides the control layer that makes these scenarios possible by:

• Abstracting tool invocation details from the agent
• Handling authentication flows securely and transparently
• Supporting real-time, bi-directional communication via WebSockets
• Enabling modular, reusable toolkits for rapid development

Introducing Agent Tool Protocol(ATP): The ATP SDK Core

The ToolkitClient is a Python SDK class that acts as the server-side agent for your tools. It allows you to:

• Register Python functions as callable tools with metadata
• Attach OAuth2 credentials and manage authentication
• Listen for incoming tool invocation requests via WebSocket
• Send back responses to the requesting agent

This SDK removes the heavy lifting of building a secure, real-time tool server and lets you focus on writing your business logic.

Getting Started with ToolKitClient

Installation

Begin by installing the ATP SDK:

pip install AgentToolProtocol 

Registering Your First Tool

Let’s create a simple tool that returns a greeting message. This function will be exposed to ATP agents as a callable tool.

from atp_sdk.clients import ToolKitClient # Initialize the client with your ATP API key and app name client = ToolKitClient(api_key="YOUR_ATP_API_KEY", app_name="my_app") @client.register_tool( function_name="hello_world", params=['name'], required_params=['name'], description="Returns a personalized greeting." ) def hello_world(**kwargs): name = kwargs.get('name', 'World') return {"message": f"Hello, {name}!"} # Start the client to listen for tool requests client.start() 

Once running, any ATP-compatible agent can invoke hello_world by passing the name parameter and receive a greeting in response.

Deep Dive: Registering Tools with OAuth2 Authentication

Many useful tools require secure authentication, especially when interacting with third-party APIs. ATP supports OAuth2 flows natively, enabling seamless integration.

Here’s an example of registering a tool that creates a company record in HubSpot:

import requests @client.register_tool( function_name="create_company", params=['name', 'domain', 'industry'], required_params=['name', 'domain', 'industry'], description="Creates a company record in HubSpot.", auth_provider="hubspot", auth_type="OAuth2", auth_with="access_token" ) def create_company(**kwargs): access_token = kwargs.get('auth_token') url = "https://api.hubapi.com/crm/v3/objects/companies" headers = { "Authorization": f"Bearer {access_token}", "Content-Type": "application/json" } data = { "properties": { "name": kwargs.get('name'), "domain": kwargs.get('domain'), "industry": kwargs.get('industry') } } response = requests.post(url, json=data, headers=headers) return response.json() 

How OAuth2 Works with ATP

• When a user connects their HubSpot account via an ATP-compatible agent interface (like ChatATP), ATP handles the OAuth2 authorization flow.
• The resulting access_token is securely passed to your tool function via the auth_token parameter.
• Your function uses this token to authenticate API requests.
• This flow ensures your tools can safely access user data without manual token management.

Anatomy of an ATP Tool

When you register a function as a tool, you provide:

Your tool function should:

• Accept **kwargs to handle dynamic parameters
• Return data in a serializable format (dict, list, string, etc.)
• Handle errors gracefully and return meaningful messages

Real-Time Tool Execution Flow

When an ATP agent invokes a tool, it sends a WebSocket message structured like this:

{ "message_type": "atp_tool_request", "payload": { "request_id": "unique-request-id", "tool_name": "create_company", "params": { "name": "Acme Corp", "domain": "acme.com", "industry": "Technology" }, "auth_token": "user-access-token" } } 

The ToolKitClient listens for such events, runs the corresponding registered function with the provided parameters and token, then sends the response back over the WebSocket connection.

This real-time, event-driven architecture enables fluid interaction between agents and tools, making it possible to build complex, multi-step AI workflows.

Managing the ToolKitClient Lifecycle

To run your tool server continuously, use:

client.start() try: while True: import time time.sleep(1) except KeyboardInterrupt: client.stop() 

This code:

• Opens a persistent WebSocket connection to ATP servers
• Automatically reconnects if the connection drops
• Dispatches incoming tool calls to registered functions
• Stops gracefully on user interrupt (Ctrl+C)

Advanced Usage and Tips

• Multiple Tools: Register as many tools as you want by stacking @register_tool decorators or defining multiple functions.
• Private vs Public Tools: Tools can be private for internal use or published to the ATP ecosystem for public agents.
• Custom Backend: Override the default ATP server URL using base_url parameter to connect to self-hosted or staging environments.
• Error Handling: Implement try-except blocks inside your tool functions to catch and report errors clearly.
• Logging: Add logging for debugging and monitoring tool usage.

Why Developers and Businesses Should Care

ATP is a game-changer for anyone building AI-powered applications:

• Custom GPTs and Claude Agents: Extend your LLMs with domain-specific tools.
• No-Code AI Builders: Empower users to create AI workflows without coding.
• SaaS Integration: Connect AI agents securely to your business’s APIs.
• Internal Automation: Build AI assistants that automate repetitive tasks with secure API access.

By giving LLMs the ability to act, not just talk, ATP enables smarter, more useful AI.

What’s Next for ATP?

The ATP ecosystem is rapidly evolving with exciting features on the horizon:

• ✅ ToolKit Store: Discover and install pre-built tools easily.
• ✅ Expanded OAuth2 Support: Integration with major platforms like Google, Microsoft, and Salesforce.
• ✅ ToolKitCollection Sync: Keep your tools synchronized across devices and environments.
• 🔜 Analytics & Rate Limiting: Monitor usage and enforce quotas.
• 🔜 CLI & Web Builder: Simplify tool creation with command-line and graphical interfaces.
• 🔜 Custom Access Control: Define fine-grained permissions and deployment environments.

Join the ATP Community

ATP is open for early adopters and developers passionate about building the future of agentic AI. If you want to:

• Create your own ToolKits
• Build AI agents with real-world capabilities
• Explore protocol-level agent infrastructure

Connect with me on X (Twitter) or email sammyfirst6@gmail.com.

Let’s move beyond static language models and build AI that can think, act, and solve problems — securely, modularly, and at scale.

🔗 ChatATP – https://chatatp.com

🧑‍💻 Docs coming soon

💬 Built by @samuelobinnachimdi

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