How to integrate Kadoa MCP with LlamaIndex

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Introduction

This guide walks you through connecting Kadoa to LlamaIndex using the Composio tool router. By the end, you'll have a working Kadoa agent that can fetch the latest data from your workflow, check crawl status for session abc123, list all pages crawled in last run through natural language commands.

This guide will help you understand how to give your LlamaIndex agent real control over a Kadoa account through Composio's Kadoa MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Kadoa with

TL;DR

Here's what you'll learn:
  • Set your OpenAI and Composio API keys
  • Install LlamaIndex and Composio packages
  • Create a Composio Tool Router session for Kadoa
  • Connect LlamaIndex to the Kadoa MCP server
  • Build a Kadoa-powered agent using LlamaIndex
  • Interact with Kadoa through natural language

What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.

Key features include:

  • ReAct Agent: Reasoning and acting pattern for tool-using agents
  • MCP Tools: Native support for Model Context Protocol
  • Context Management: Maintain conversation context across interactions
  • Async Support: Built for async/await patterns

What is the Kadoa MCP server, and what's possible with it?

The Kadoa MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Kadoa account. It provides structured and secure access to your data extraction workflows, so your agent can launch crawls, monitor sessions, retrieve extracted data, and manage workflow configurations automatically on your behalf.

  • Automated workflow monitoring and management: Ask your agent to fetch workflow configurations, enable data validation, or get the latest results from any extraction workflow you have set up.
  • Crawling session control: Have your agent check the status of crawl sessions, list all crawled pages, and pull the raw content (HTML or Markdown) from any page processed by a workflow.
  • Notification channel setup and retrieval: Direct your agent to create notification channels, list available notification event types, and fetch specific channel configurations for streamlined alerting.
  • Location and environment awareness: Let your agent retrieve all supported locations to ensure workflows run in the right environment before launching new extraction jobs.
  • Seamless data access: Instruct your agent to quickly get the most recent data output from any workflow, keeping your automations and dashboards always up to date.

Supported Tools & Triggers

Tools
Bulk Approve Validation RulesTool to bulk approve preview validation rules for a workflow.
Create Crawl ConfigTool to create a new crawling configuration in Kadoa.
Create Notification ChannelTool to create a notification channel for alerts delivery.
Create SchemaCreate a new data schema with specified fields and entity type.
Create Support IssueTool to create a support ticket in Kadoa.
Create Workflow TriggerTool to create a trigger that fires when a source workflow emits an event.
Delete All Validation RulesTool to soft-delete all validation rules for a specific workflow with optional audit trail.
Delete Crawl ConfigurationTool to delete a crawling configuration by its config ID.
Delete Notification ChannelTool to delete a notification channel by its ID.
Delete SchemaTool to delete a schema and all its revisions.
Delete Validation RuleTool to delete a validation rule from a Kadoa workflow.
Delete Validation Rules (Bulk)Tool to bulk delete multiple validation rules for a workflow.
Delete WorkflowDelete a workflow permanently from your Kadoa account.
Delete Workflow TriggerTool to delete a trigger from a Kadoa workflow.
Disable Validation RuleTool to disable a validation rule with a mandatory reason.
Enable Data ValidationTool to enable data validation on a specified workflow.
Execute Bulk Workflow OperationsExecute actions on multiple workflows at once.
Export Activity EventsTool to export activity events from audit logs to CSV format for compliance and audit purposes.
Export Activity WorkflowsTool to export workflow configurations and metadata as CSV for portfolio reviews and compliance reporting.
Get Workflow by IDRetrieve detailed configuration of a workflow by its ID.
Get all locationsRetrieves all available scraping proxy locations (countries) supported by Kadoa.
Get Crawl Bucket DataTool to retrieve file content from the Kadoa crawling bucket (HTML or screenshot).
Get Crawl ConfigurationTool to retrieve a crawling configuration by its ID.
Get Crawled Page ContentTool to retrieve content of a crawled page.
Get Crawled PagesTool to list pages crawled during a session.
Get Crawl StatusTool to fetch current status of a crawling session.
Get Event Type DetailsTool to retrieve details for a specific notification event type.
Get Notification Event TypesTool to retrieve supported notification event types.
Get Latest Workflow DataRetrieves the extracted data from a Kadoa workflow's most recent run (or a specific run if runId is provided).
Get Latest Workflow ValidationRetrieves the latest validation results for the most recent job of a workflow.
Get Notification ChannelTool to retrieve details of a specific notification channel.
Get Notification LogsTool to retrieve notification event logs with optional filtering by workflow, event type, and date range.
Get Notification SettingRetrieves a specific notification setting by its unique identifier.
Get Schema by IDRetrieve a specific schema by its unique identifier.
Get Validation AnomaliesTool to retrieve all anomalies for a specific validation.
Get Validation Anomalies By RuleTool to retrieve anomalies for a specific validation rule.
Get Validation ConfigurationTool to retrieve the data validation configuration for a specific workflow.
Get Validation RuleTool to retrieve a specific validation rule by its ID.
Get Workflow Audit LogRetrieve audit log entries for a workflow.
Get Workflow JobTool to retrieve the current status and telemetry information for a specific workflow job.
Get Workflow Run HistoryTool to fetch workflow run history.
Get WorkflowsRetrieve a paginated list of workflows with optional filtering.
Get Workflow TriggerTool to retrieve a specific trigger for a workflow.
Get Workflow Validation ResultsRetrieves the latest validation results for a specific workflow job.
Get Workspace DetailsTool to retrieve detailed information about a workspace (user, team, or organization).
List Activity EventsTool to retrieve activity events from audit logs with basic filtering and pagination.
List ChangesTool to retrieve all data changes detected across workflows in your Kadoa account.
List Crawl SessionsTool to retrieve a paginated list of crawling sessions with optional filtering.
List Job ValidationsTool to list all validation runs for a specific job with pagination support.
List Notification ChannelsTool to retrieve all notification channels configured for the account.
List Notification SettingsTool to retrieve all notification settings, with optional filtering by workflow ID or event type.
List SchemasTool to retrieve all schemas accessible by the authenticated user.
List Support StatesTool to retrieve available support issue states.
List Validation RulesTool to list all data validation rules with optional pagination and filtering.
List Workflow TriggersTool to get all triggers where the specified workflow is the source.
Pause Crawl SessionTool to pause an active crawling session.
Pause WorkflowTool to pause a running or scheduled workflow.
Create Advanced WorkflowTool to create an advanced workflow.
Start Crawl SessionStarts a new web crawling session to crawl and index pages from a website.
Create Notification SettingTool to create a notification setting linking channels to events.
Send Test NotificationSends a test notification event to verify notification channel configurations are working correctly.
Subscribe to Webhook EventsTool to subscribe to specified webhook events.
Create WorkflowCreate a new Kadoa web scraping workflow.
Configure Workflow MonitoringConfigure monitoring and scheduling for a Kadoa workflow to detect data changes.
Generate Workflow Validation RuleGenerate an AI-powered data validation rule for a Kadoa workflow.
Update Notification ChannelTool to update an existing notification channel.
Resume Crawl SessionTool to resume a paused crawling session.
Resume WorkflowResumes a paused, preview, or error workflow.
Run Ad-hoc ExtractionTool to synchronously extract data from a URL using a given template.
Run WorkflowTool to trigger a workflow to run immediately.
Schedule Validation JobTool to schedule a data validation job for a specific workflow job.
Unsubscribe from Webhook EventsUnsubscribe from webhook event notifications by deleting a notification setting.
Update Notification SettingsTool to update existing notification settings for events.
Update SchemaTool to update an existing Kadoa schema.
Update Validation ConfigurationTool to update the complete data validation configuration including alerting settings for a specific workflow.
Update Workflow MetadataTool to update workflow metadata such as name, description, tags, and configuration settings.
Update Workflow TriggerTool to update trigger properties including event type and enabled status.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before you begin, make sure you have:
  • Python 3.8/Node 16 or higher installed
  • A Composio account with the API key
  • An OpenAI API key
  • A Kadoa account and project
  • Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Kadoa

OpenAI API key (OPENAI_API_KEY)
  • Go to the OpenAI dashboard
  • Create an API key if you don't have one
  • Assign it to OPENAI_API_KEY in .env
Composio API key and user ID
  • Log into the Composio dashboard
  • Copy your API key from Settings
    • Use this as COMPOSIO_API_KEY
  • Pick a stable user identifier (email or ID)
    • Use this as COMPOSIO_USER_ID

Installing dependencies

pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv

Create a new Python project and install the necessary dependencies:

  • composio-llamaindex: Composio's LlamaIndex integration
  • llama-index: Core LlamaIndex framework
  • llama-index-llms-openai: OpenAI LLM integration
  • llama-index-tools-mcp: MCP client for LlamaIndex
  • python-dotenv: Environment variable management

Set environment variables

bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id

Create a .env file in your project root:

These credentials will be used to:

  • Authenticate with OpenAI's GPT-5 model
  • Connect to Composio's Tool Router
  • Identify your Composio user session for Kadoa access

Import modules

import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

Create a new file called kadoa_llamaindex_agent.py and import the required modules:

Key imports:

  • asyncio: For async/await support
  • Composio: Main client for Composio services
  • LlamaIndexProvider: Adapts Composio tools for LlamaIndex
  • ReActAgent: LlamaIndex's reasoning and action agent
  • BasicMCPClient: Connects to MCP endpoints
  • McpToolSpec: Converts MCP tools to LlamaIndex format

Load environment variables and initialize Composio

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set in the environment")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment")

What's happening:

This ensures missing credentials cause early, clear errors before the agent attempts to initialise.

Create a Tool Router session and build the agent function

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["kadoa"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform Kadoa actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Kadoa actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)

What's happening here:

  • We create a Composio client using your API key and configure it with the LlamaIndex provider
  • We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, kadoa)
  • The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
  • LlamaIndex will connect to this endpoint to dynamically discover and use the available Kadoa tools.
  • The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.

Create an interactive chat loop

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

What's happening here:

  • We're creating a direct terminal interface to chat with your Kadoa database
  • The LLM's responses are streamed to the CLI for faster interaction.
  • The agent uses context to maintain conversation history
  • You can type 'quit' or 'exit' to stop the chat loop gracefully
  • Agent responses and any errors are displayed in a clear, readable format

Define the main entry point

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

What's happening here:

  • We're orchestrating the entire application flow
  • The agent gets built with proper error handling
  • Then we kick off the interactive chat loop so you can start talking to Kadoa

Run the agent

npx ts-node llamaindex-agent.ts

When prompted, authenticate and authorise your agent with Kadoa, then start asking questions.

Complete Code

Here's the complete code to get you started with Kadoa and LlamaIndex:

import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["kadoa"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform Kadoa actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Kadoa actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")

Conclusion

You've successfully connected Kadoa to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:
  • Tool Router dynamically exposes Kadoa tools through an MCP endpoint
  • LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
  • The agent becomes more capable without increasing prompt size
  • Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

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FAQ

What are the differences in Tool Router MCP and Kadoa MCP?

With a standalone Kadoa MCP server, the agents and LLMs can only access a fixed set of Kadoa tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Kadoa and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LlamaIndex?

Yes, you can. LlamaIndex fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Kadoa tools.

Can I manage the permissions and scopes for Kadoa while using Tool Router?

Yes, absolutely. You can configure which Kadoa scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Kadoa data and credentials are handled as safely as possible.

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