How to integrate Docsbot ai MCP with LlamaIndex

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Introduction

This guide walks you through connecting Docsbot ai to LlamaIndex using the Composio tool router. By the end, you'll have a working Docsbot ai agent that can list all bots for your team, generate support ticket from recent chat, update bot description to new branding through natural language commands.

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

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

Also integrate Docsbot ai 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 Docsbot ai
  • Connect LlamaIndex to the Docsbot ai MCP server
  • Build a Docsbot ai-powered agent using LlamaIndex
  • Interact with Docsbot ai 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 Docsbot ai MCP server, and what's possible with it?

The Docsbot ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Docsbot ai account. It provides structured and secure access to your Docsbot ai bots, teams, and conversation data, so your agent can perform actions like creating bots, managing teams, generating support tickets, and analyzing user questions on your behalf.

  • Custom bot creation and management: Instantly create new Docsbot ai bots or update existing ones, letting your agent provision and configure bots for different documentation needs.
  • Team administration and overview: Allow your agent to fetch details about your teams or list all teams associated with your account, making it easier to manage collaboration and bot access.
  • Automated support ticket generation: Easily convert chatbot conversations into structured support tickets, so your agent can help streamline customer support and issue tracking.
  • Bot question and source analytics: Retrieve lists of questions asked to your bots or review all data sources connected to a given bot, empowering your agent to surface insights or monitor bot effectiveness.
  • Seamless bot and data cleanup: Direct your agent to delete bots or manage bot sources, helping you keep your Docsbot ai environment tidy and up to date.

Supported Tools & Triggers

Tools
Capture Conversation LeadTool to capture lead information by updating conversation metadata and saving the lead.
Create BotTool to create a new bot within a team.
Create Bot SourceTool to create a new source for a bot.
Create WebhookTool to create a new webhook subscription for a bot.
Delete BotTool to delete a specific bot by its ID.
Delete ConversationTool to delete a specific conversation by its ID.
Delete LeadTool to delete a specific lead by ID.
Delete QuestionTool to delete a specific question from history.
Delete SourceTool to delete a specific source from a bot by its ID.
Delete WebhookTool to delete a webhook (unsubscribe) by its ID.
Generate Conversation TicketGenerates a structured support ticket from a Chat Agent conversation.
Get Bot DetailsTool to fetch details of a specific bot by ID within a team.
Get Bot Monthly ReportsTool to retrieve monthly statistical reports for a bot.
Get Bot StatisticsTool to retrieve comprehensive statistics and analytics for a bot over a time period or date range.
Get Source DetailsTool to retrieve detailed information about a specific source by its ID.
Get Team DetailsTool to fetch details of a specific team by its ID.
Get Upload URLGet a presigned upload URL for uploading files as sources.
Get Webhook DetailsTool to retrieve details of a specific webhook by ID.
List Team BotsList all bots for a given team.
List Bot ConversationsTool to list conversation history for a bot with pagination.
List Bot LeadsTool to list captured leads for a bot with pagination and date filtering.
List QuestionsTool to list all questions asked of a specific bot.
List Research JobsTool to list all deep research jobs for a bot with pagination support.
List Bot SourcesRetrieves a paginated list of all sources for a specific bot within a team.
List Team MembersTool to list all members of a team including their roles.
List TeamsTool to list all teams.
List Bot WebhooksList all registered webhooks for a bot.
Rate AnswerTool to rate an answer from chat APIs as positive (1), neutral (0), or negative (-1).
Refresh SourceTool to refresh a source to re-index its content.
Semantic Search Bot ContentTool to perform semantic search on a bot's indexed content.
Test Escalated WebhookTool to trigger a test delivery of the conversation.
Test Lead WebhookTool to trigger a test lead webhook delivery.
Test Research WebhookTool to trigger a deep research webhook delivery test.
Trigger Rated Webhook TestTool to trigger a conversation.
Update BotUpdate a bot's configuration settings such as name, description, model, temperature, and appearance.
Update TeamTool to update specific fields for a team.
Update WebhookTool to update a webhook's status, target URL, label, or expiration date.
Upload File to Cloud StorageUpload a file to cloud storage via a presigned URL.

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 Docsbot ai account and project
  • Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Docsbot ai

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 Docsbot ai 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 docsbot ai_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=["docsbot_ai"],
    )

    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 Docsbot ai actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Docsbot ai 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, docsbot ai)
  • 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 Docsbot ai 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 Docsbot ai 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 Docsbot ai

Run the agent

npx ts-node llamaindex-agent.ts

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

Complete Code

Here's the complete code to get you started with Docsbot ai 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=["docsbot_ai"],
    )

    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 Docsbot ai actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Docsbot ai 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 Docsbot ai to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:
  • Tool Router dynamically exposes Docsbot ai 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 Docsbot ai MCP?

With a standalone Docsbot ai MCP server, the agents and LLMs can only access a fixed set of Docsbot ai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Docsbot ai 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 Docsbot ai tools.

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

Yes, absolutely. You can configure which Docsbot ai 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 Docsbot ai data and credentials are handled as safely as possible.

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