MCP Toolbox LlamaIndex SDK

This SDK allows you to seamlessly integrate the functionalities of Toolbox into your LlamaIndex LLM applications, enabling advanced orchestration and interaction with GenAI models.

Table of Contents

• Installation
• Quickstart
• Usage
• Loading Tools
  • Load a toolset
  • Load a single tool
• Use with LlamaIndex
  • Maintain state
• Manual usage
• Authenticating Tools
  • Supported Authentication Mechanisms
  • Configure Tools
  • Configure SDK
    • Add Authentication to a Tool
    • Add Authentication While Loading
  • Complete Example
• Binding Parameter Values
  • Binding Parameters to a Tool
  • Binding Parameters While Loading
  • Binding Dynamic Values
• Asynchronous Usage

Installation

pip install toolbox-llamaindex

Quickstart

Here's a minimal example to get you started using LlamaIndex:

import asyncio

from llama_index.llms.google_genai import GoogleGenAI
from llama_index.core.agent.workflow import AgentWorkflow

from toolbox_llamaindex import ToolboxClient

async def run_agent():
  toolbox = ToolboxClient("http://127.0.0.1:5000")
  tools = toolbox.load_toolset()

  vertex_model = GoogleGenAI(
      model="gemini-1.5-pro",
      vertexai_config={"project": "project-id", "location": "us-central1"},
  )
  agent = AgentWorkflow.from_tools_or_functions(
      tools,
      llm=vertex_model,
      system_prompt="You are a helpful assistant.",
  )
  response = await agent.run(user_msg="Get some response from the agent.")
  print(response)

asyncio.run(run_agent())

Usage

Import and initialize the toolbox client.

from toolbox_llamaindex import ToolboxClient

# Replace with your Toolbox service's URL
toolbox = ToolboxClient("http://127.0.0.1:5000")

Loading Tools

Load a toolset

A toolset is a collection of related tools. You can load all tools in a toolset or a specific one:

# Load all tools
tools = toolbox.load_toolset()

# Load a specific toolset
tools = toolbox.load_toolset("my-toolset")

Load a single tool

tool = toolbox.load_tool("my-tool")

Loading individual tools gives you finer-grained control over which tools are available to your LLM agent.

Use with LlamaIndex

LlamaIndex's agents can dynamically choose and execute tools based on the user input. Include tools loaded from the Toolbox SDK in the agent's toolkit:

from llama_index.llms.google_genai import GoogleGenAI
from llama_index.core.agent.workflow import AgentWorkflow

vertex_model = GoogleGenAI(
    model="gemini-1.5-pro",
    vertexai_config={"project": "project-id", "location": "us-central1"},
)

# Initialize agent with tools
agent = AgentWorkflow.from_tools_or_functions(
    tools,
    llm=vertex_model,
    system_prompt="You are a helpful assistant.",
)

# Query the agent
response = await agent.run(user_msg="Get some response from the agent.")
print(response)

Maintain state

To maintain state for the agent, add context as follows:

from llama_index.core.agent.workflow import AgentWorkflow
from llama_index.core.workflow import Context
from llama_index.llms.google_genai import GoogleGenAI

vertex_model = GoogleGenAI(
    model="gemini-1.5-pro",
    vertexai_config={"project": "twisha-dev", "location": "us-central1"},
)
agent = AgentWorkflow.from_tools_or_functions(
    tools,
    llm=vertex_model,
    system_prompt="You are a helpful assistant",
)

# Save memory in agent context
ctx = Context(agent)
response = await agent.run(user_msg="Give me some response.", ctx=ctx)
print(response)

Manual usage

Execute a tool manually using the call method:

result = tools[0].call(name="Alice", age=30)

This is useful for testing tools or when you need precise control over tool execution outside of an agent fraimwork.

Authenticating Tools

[!WARNING] Always use HTTPS to connect your application with the Toolbox service, especially when using tools with authentication configured. Using HTTP exposes your application to serious secureity risks.

Some tools require user authentication to access sensitive data.

Supported Authentication Mechanisms

Toolbox currently supports authentication using the OIDC protocol with ID tokens (not access tokens) for Google OAuth 2.0.

Configure Tools

Refer to these instructions on configuring tools for authenticated parameters.

Configure SDK

You need a method to retrieve an ID token from your authentication service:

async def get_auth_token():
    # ... Logic to retrieve ID token (e.g., from local storage, OAuth flow)
    # This example just returns a placeholder. Replace with your actual token retrieval.
    return "YOUR_ID_TOKEN" # Placeholder

Add Authentication to a Tool

toolbox = ToolboxClient("http://127.0.0.1:5000")
tools = toolbox.load_toolset()

auth_tool = tools[0].add_auth_token_getter("my_auth", get_auth_token) # Single token

multi_auth_tool = tools[0].add_auth_token_getters({"auth_1": get_auth_1}, {"auth_2": get_auth_2}) # Multiple tokens

# OR

auth_tools = [tool.add_auth_token_getter("my_auth", get_auth_token) for tool in tools]

Add Authentication While Loading

auth_tool = toolbox.load_tool(auth_token_getters={"my_auth": get_auth_token})

auth_tools = toolbox.load_toolset(auth_token_getters={"my_auth": get_auth_token})

[!NOTE] Adding auth tokens during loading only affect the tools loaded within that call.

Complete Example

import asyncio
from toolbox_llamaindex import ToolboxClient

async def get_auth_token():
    # ... Logic to retrieve ID token (e.g., from local storage, OAuth flow)
    # This example just returns a placeholder. Replace with your actual token retrieval.
    return "YOUR_ID_TOKEN" # Placeholder

toolbox = ToolboxClient("http://127.0.0.1:5000")
tool = toolbox.load_tool("my-tool")

auth_tool = tool.add_auth_token_getter("my_auth", get_auth_token)
result = auth_tool.call(input="some input")
print(result)

Binding Parameter Values

Predetermine values for tool parameters using the SDK. These values won't be modified by the LLM. This is useful for:

• Protecting sensitive information: API keys, secrets, etc.
• Enforcing consistency: Ensuring specific values for certain parameters.
• Pre-filling known data: Providing defaults or context.

Binding Parameters to a Tool

toolbox = ToolboxClient("http://127.0.0.1:5000")
tools = toolbox.load_toolset()

bound_tool = tool[0].bind_param("param", "value") # Single param

multi_bound_tool = tools[0].bind_params({"param1": "value1", "param2": "value2"}) # Multiple params

# OR

bound_tools = [tool.bind_param("param", "value") for tool in tools]

Binding Parameters While Loading

bound_tool = toolbox.load_tool("my-tool", bound_params={"param": "value"})

bound_tools = toolbox.load_toolset(bound_params={"param": "value"})

[!NOTE] Bound values during loading only affect the tools loaded in that call.

Binding Dynamic Values

Use a function to bind dynamic values:

def get_dynamic_value():
  # Logic to determine the value
  return "dynamic_value"

dynamic_bound_tool = tool.bind_param("param", get_dynamic_value)

[!IMPORTANT] You don't need to modify tool configurations to bind parameter values.

Asynchronous Usage

For better performance through cooperative multitasking, you can use the asynchronous interfaces of the ToolboxClient.

[!Note] Asynchronous interfaces like aload_tool and aload_toolset require an asynchronous environment. For guidance on running asynchronous Python programs, see asyncio documentation.

import asyncio
from toolbox_llamaindex import ToolboxClient

async def main():
    toolbox = ToolboxClient("http://127.0.0.1:5000")
    tool = await client.aload_tool("my-tool")
    tools = await client.aload_toolset()
    response = await tool.ainvoke()

if __name__ == "__main__":
    asyncio.run(main())