How to integrate Rocketlane MCP with LlamaIndex

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Introduction

This guide walks you through connecting Rocketlane to LlamaIndex using the Composio tool router. By the end, you'll have a working Rocketlane agent that can create a new onboarding project for acme corp, log two hours to client implementation task, archive completed projects from last quarter, get detailed info for company with id 12345 through natural language commands.

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

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

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

The Rocketlane MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Rocketlane account. It provides structured and secure access to your onboarding projects, tasks, and customer data, so your agent can perform actions like creating tasks, managing companies, tracking time entries, and handling project organization on your behalf.

  • Project and company management: Easily direct your agent to create new projects or companies, retrieve detailed company info, and keep your workspace organized.
  • Task creation and deletion: Have your agent add new tasks to any project or swiftly delete outdated tasks using their unique identifiers.
  • Time entry tracking: Log time spent on tasks or projects, review details, or delete time entries for accurate billing and reporting.
  • Custom field insights: Retrieve all available custom fields or fetch specific field details to tailor onboarding workflows to your needs.
  • Project archiving and cleanup: Archive completed projects for future reference or permanently delete projects when they're no longer needed, keeping your workspace tidy.

Supported Tools & Triggers

Tools
Archive Project by IDArchives a specific project based on its unique identifier.
Create CompanyCreates a new company (account) in rocketlane.
Create TaskCreates a new task.
Create Time EntryTool to create a new time entry in rocketlane.
Delete ProjectThis tool allows users to permanently delete a project in rocketlane.
Delete Task By IDDelete a specific task using its unique identifier (taskid).
Delete Time Entry by IDDelete a specific time entry using its unique identifier (timeentryid).
Get All FieldsRetrieve all custom fields available in the system.
Get CompanyThis tool retrieves detailed information about a specific company/account in rocketlane by its id.
Get Field By IDRetrieve detailed information about a specific custom field using its unique identifier (fieldid).
Get Project by IDRetrieves detailed information about a specific project using its unique identifier.
Get Task By IdRetrieve extensive information about a specific task using the task's unique identifier (taskid).
Get Template By IDRetrieve detailed information about a specific template using its unique identifier (templateid).
Get Time EntriesTool to retrieve all time entries from rocketlane.
Get Time Entry By IDRetrieve detailed information about a specific time entry using its unique identifier (timeentryid).
Get User By IDRetrieve detailed information about a specific user using their unique identifier (userid).
List CompaniesThis tool retrieves a list of all companies/accounts in rocketlane.
List Company FieldsThis tool retrieves a list of all available company/account fields in rocketlane.
List Company Note FieldsThis tool retrieves a list of all available note fields for companies in rocketlane.
List CurrenciesReturns a predefined list of commonly used currencies since rocketlane api doesn't provide a dedicated currencies endpoint.
List Customer UsersList customer users.
List Project FieldsThis tool retrieves a list of all project fields in rocketlane, including both default and custom fields.
List Project PhasesThis tool retrieves a list of project phases from rocketlane.
List ProjectsThis tool retrieves a list of all projects in the rocketlane instance.
List Task FieldsThis tool retrieves a list of all task fields in rocketlane.
List TemplatesThis tool retrieves a list of all available templates in rocketlane.
List UsersThis tool retrieves all users in the rocketlane instance.
List Vendor UsersList vendor users by filtering users with type 'partner'.
Retrieve Subscription DetailsRetrieves detailed information about the current subscription.
Search User By EmailSearch user by email id.
Update CompanyThis tool updates an existing company/account in rocketlane.
Update Project By IdUpdates an existing project's details using its unique identifier.
Update Time Entry by IDUpdate existing time entry details using its unique identifier (timeentryid).

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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 Rocketlane account and project
  • Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Rocketlane

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 Rocketlane 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 rocketlane_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=["rocketlane"],
    )

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

Run the agent

npx ts-node llamaindex-agent.ts

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

Complete Code

Here's the complete code to get you started with Rocketlane 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=["rocketlane"],
    )

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

How to build Rocketlane MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Rocketlane MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Rocketlane MCP with Claude Agent SDK

Google ADK

Google ADK

Use Rocketlane MCP with Google ADK

LangChain

LangChain

Use Rocketlane MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Rocketlane MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Rocketlane MCP with Mastra AI

CrewAI

CrewAI

Use Rocketlane MCP with CrewAI

FAQ

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

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

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

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

Used by agents from

Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

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