AgentToolProtocol 0.1.2

Agent Tool Protocol SDK

Agent Tool Protocol(ATP)

A Python SDK for registering, exposing, and serving your own Python functions as tools via the ATP platform. Supports secure OAuth2 flows, dynamic tool registration, and real-time tool invocation via WebSocket.

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Table of Contents

• Installation
• Quick Start
• Class: ToolKitClient
  • Constructor
  • register_tool
  • start
  • stop
• Tool Function Requirements
• WebSocket Events
• Error Handling
• Examples
• Advanced Usage
• License

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Installation

pip install AgentToolProtocol

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Quick Start

from atp_sdk.clients import ToolKitClient
import requests

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 greeting.",
    auth_provider=None, auth_type=None, auth_with=None
)
def hello_world(**kwargs):
    return {"message": f"Hello, {kwargs.get('name', 'World')}!"}

client.start()

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Class: ToolKitClient

Constructor

ToolKitClient(
    api_key: str,
    app_name: str,
    base_url: str = "https://chatatp-backend.onrender.com"
)

Parameters:

• api_key (str): Your ATP API key.
• app_name (str): Name of your application.
• base_url (str, optional): ATP Server backend URL. Defaults to chatatp-backend.onrender.com.

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register_tool

Registers a Python function as a tool with the ATP platform.

@client.register_tool(
    function_name: str,
    params: list[str],
    required_params: list[str],
    description: str,
    auth_provider: Optional[str],
    auth_type: Optional[str],
    auth_with: Optional[str]
)
def my_tool(**kwargs):
    ...

Arguments:

• function_name: Unique name for the tool.
• params: List of all parameter names.
• required_params: List of required parameter names.
• description: Human-readable description.
• auth_provider: Name of OAuth2 provider (e.g., "hubspot", "google"), or None.
• auth_type: Auth type (e.g., "OAuth2"), or None.
• auth_with: Name of the token parameter (e.g., "access_token"), or None.

Returns:
A decorator to wrap your function.

Example:

@client.register_tool(
    function_name="create_company",
    params=['name', 'domain', 'industry'],
    required_params=['name', 'domain', 'industry'],
    description="Creates a company 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()

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start

Starts the WebSocket client and begins listening for tool requests.

client.start()

• Keeps the main thread alive.
• Handles reconnections automatically.

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stop

Stops the WebSocket client and closes the connection.

client.stop()

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Tool Function Requirements

• Must accept all parameters as **kwargs.
• If your tool requires authentication, expect auth_token in kwargs.
• Return a serializable object (dict, str, etc).

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WebSocket Events

Tool Registration

Upon registration, your tool is announced to the ATP backend and available for invocation.

Tool Invocation

When a tool request is received, your function is called with the provided parameters and (if needed) auth_token.

Example incoming message:

{
  "message_type": "atp_tool_request",
  "payload": {
    "request_id": "uuid",
    "tool_name": "create_company",
    "params": {"name": "Acme", "domain": "acme.com", "industry": "Tech"},
    "auth_token": "ACCESS_TOKEN"
  }
}

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Error Handling

• If your function raises an exception, the error is caught and returned as:

  {"error": "Error message"}
  

• If required parameters are missing, an error is returned.
• If auth_token is required but missing, an error is returned.

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Examples

Minimal Tool

@client.register_tool(
    function_name="echo",
    params=['text'],
    required_params=['text'],
    description="Echoes the input text.",
    auth_provider=None, auth_type=None, auth_with=None
)
def echo(**kwargs):
    return {"echo": kwargs.get('text')}

Tool with OAuth2

@client.register_tool(
    function_name="get_contacts",
    params=['auth_token'],
    required_params=['auth_token'],
    description="Fetches contacts from HubSpot.",
    auth_provider="hubspot", auth_type="OAuth2", auth_with="access_token"
)
def get_contacts(**kwargs):
    access_token = kwargs.get('auth_token')
    url = "https://api.hubapi.com/crm/v3/objects/contacts"
    headers = {"Authorization": f"Bearer {access_token}"}
    response = requests.get(url, headers=headers)
    return response.json()

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Class: LLMClient

The LLMClient lets you connect to the ATP Agent Server, retrieve toolkit context, and execute tools or workflows using JSON payloads—perfect for LLM-based agents.

Constructor

from atp_sdk.clients import LLMClient

llm_client = LLMClient(
    api_key="YOUR_ATP_API_KEY"
)

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get_toolkit_context

Retrieves the toolkit context and system instructions for a given toolkit and user prompt.

context = llm_client.get_toolkit_context(
    toolkit_id="your_toolkit_id",
    user_prompt="What do you want to achieve?"
)

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call_toolkit

Executes a tool or workflow on the ATP server.

response = llm_client.call_toolkit(
    toolkit_id="your_toolkit_id",
    json_response=json.dumps({
        "function": "hello_world",
        "parameters": {"name": "Alice"},
        "task_title": "Say hello",
        "execution_type": "remote"
    })
)
print(response)

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Example: Using LLMClient with OpenAI, Anthropic, and Mistral AI

You can use any LLM to generate the JSON workflow, then execute each step with LLMClient.

1. OpenAI (GPT-4o)

import openai
from atp_sdk.clients import LLMClient

llm_client = LLMClient(api_key="YOUR_ATP_API_KEY")

# Get toolkit context and system prompt
context = llm_client.get_toolkit_context(toolkit_id="your_toolkit_id", user_prompt="Create a company and then list contacts.")

# Use OpenAI to generate the workflow JSON
response = openai.ChatCompletion.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": context},
        {"role": "user", "content": "Create a company and then list contacts."}
    ]
)
workflow_json = response.choices[0].message.content

# Parse and execute each workflow step
import json
workflow = json.loads(workflow_json)
if "workflows" in workflow:
    results = []
    for step in workflow["workflows"]:
        result = llm_client.call_toolkit(
            toolkit_id="your_toolkit_id",
            json_response=json.dumps(step)
        )
        results.append(result)
else:
    result = llm_client.call_toolkit(
        toolkit_id="your_toolkit_id",
        json_response=workflow_json
    )

2. Anthropic (Claude)

import anthropic
from atp_sdk.clients import LLMClient

llm_client = LLMClient(api_key="YOUR_ATP_API_KEY")
context = llm_client.get_toolkit_context(toolkit_id="your_toolkit_id", user_prompt="...")

client = anthropic.Anthropic(api_key="YOUR_ANTHROPIC_API_KEY")
response = client.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": context}
    ]
)
workflow_json = response.content[0].text

# Execute as above

3. Mistral AI

from mistralai.client import MistralClient
from atp_sdk.clients import LLMClient

llm_client = LLMClient(api_key="YOUR_ATP_API_KEY")
context = llm_client.get_toolkit_context(toolkit_id="your_toolkit_id", user_prompt="...")

client = MistralClient(api_key="YOUR_MISTRAL_API_KEY")
response = client.chat(
    model="mistral-large-latest",
    messages=[{"role": "user", "content": context}]
)
workflow_json = response.choices[0].message.content

# Execute as above

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Handling Multi-Step Workflows

When the LLM returns a workflow with multiple steps:

import json

workflow = json.loads(workflow_json)
if "workflows" in workflow:
    results = []
    for step in workflow["workflows"]:
        result = llm_client.call_toolkit(
            toolkit_id="your_toolkit_id",
            json_response=json.dumps(step)
        )
        results.append(result)
else:
    result = llm_client.call_toolkit(
        toolkit_id="your_toolkit_id",
        json_response=workflow_json
    )

• For each step, call llm_client.call_toolkit with the step JSON.
• Collect and process results as needed.
• If a step has "depends_on", you can pass outputs from previous steps as needed.

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Example: Full Workflow

from atp_sdk.clients import LLMClient
import openai
import json

llm_client = LLMClient(api_key="YOUR_ATP_API_KEY")
context = llm_client.get_toolkit_context(toolkit_id="your_toolkit_id", user_prompt="...")

response = openai.ChatCompletion.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": context},
        {"role": "user", "content": "Do a multi-step task."}
    ]
)
workflow_json = response.choices[0].message.content
workflow = json.loads(workflow_json)

results = []
if "workflows" in workflow:
    for step in workflow["workflows"]:
        result = llm_client.call_toolkit(
            toolkit_id="your_toolkit_id",
            json_response=json.dumps(step)
        )
        results.append(result)
else:
    result = llm_client.call_toolkit(
        toolkit_id="your_toolkit_id",
        json_response=workflow_json
    )
    results.append(result)

print(results)

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Tip:
Always ensure the LLM returns valid JSON as described in the toolkit context instructions.

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Advanced Usage

Custom Backend

client = ToolKitClient(
    api_key="YOUR_API_KEY",
    app_name="my_app",
    base_url="https://your-backend.example.com"
)

Multiple Tools

@client.register_tool(...)
def tool1(**kwargs): ...

@client.register_tool(...)
def tool2(**kwargs): ...

Keeping the Main Thread Alive

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

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License

MIT License.
See LICENSE for details.

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Feedback & Issues

For bug reports or feature requests, please open an issue on GitHub.

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Happy coding! 🚀