How to integrate DeployHQ MCP with LlamaIndex

GET STARTED FOR FREE
Framework Integration Gradient
DeployHQ Logo
LlamaIndex Logo
divider

Introduction

This guide walks you through connecting DeployHQ to LlamaIndex using the Composio tool router. By the end, you'll have a working DeployHQ agent that can trigger a deployment for project x, list all deployments for project y, get status of last deployment through natural language commands.

This guide will help you understand how to give your LlamaIndex agent real control over a DeployHQ account through Composio's DeployHQ 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 DeployHQ
  • Connect LlamaIndex to the DeployHQ MCP server
  • Build a DeployHQ-powered agent using LlamaIndex
  • Interact with DeployHQ 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 DeployHQ MCP server, and what's possible with it?

The DeployHQ MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your DeployHQ account. It provides structured and secure access so your agent can perform DeployHQ operations on your behalf.

Supported Tools & Triggers

Tools
Delete CommandTool to delete a command from a specified project.
Delete ProjectTool to delete a project from DeployHQ.
Delete Build Cache FileTool to delete an existing build cache file from a project.
Delete Excluded File RuleTool to delete an existing excluded file rule from a project.
Delete Server GroupTool to delete a server group from a project using the DeployHQ API.
Delete TemplateTool to delete a template by its unique permalink.
Get ProjectsTool to retrieve all projects from DeployHQ account.
Get ProjectTool to view an existing project in DeployHQ.
Get Project Build Known HostsTool to list all known hosts within a project using DeployHQ API.
Get Project CommandsTool to retrieve all SSH commands configured for a project.
Get Project Config FilesTool to retrieve a list of all config files in a DeployHQ project.
Get Project DeploymentsTool to retrieve a paginated list of all deployments in a project.
Get Project Excluded FilesTool to list all excluded files within a project template.
Get Config FileTool to view a specific config file in a DeployHQ project.
Get Excluded FileTool to view a specific excluded file in a DeployHQ project.
Get Server GroupTool to view a specific server group in a DeployHQ project.
Get Project RepositoryTool to view repository details for a specific project in DeployHQ.
Get Repository BranchesTool to view all available branches in the connected repository for a project.
Get Repository Commit InfoTool to view detailed information about a specific revision in a project's connected repository.
Get Latest Repository RevisionTool to view the latest remote revision of your repository.
Get Recent Commits and TagsTool to view up to 15 most recent revisions and up to 15 most recent tags in a specific branch.
Get Project Scheduled DeploymentsTool to retrieve all upcoming scheduled deployments for a project.
Get Project Server GroupsTool to retrieve all server groups configured for a project.
Get Project ServersTool to retrieve all servers configured for a project.
Get TemplatesTool to retrieve all templates from DeployHQ account.
Get Public TemplateTool to retrieve a specific public template from DeployHQ.
Get Public TemplatesTool to retrieve publicly available deployment templates from DeployHQ.
Update ProjectTool to update project settings in DeployHQ.
Update Build Cache FileTool to update an existing build cache file in a project.
Update Build CommandTool to update an existing build command in a project.
Update Language VersionTool to update the version of a language in a project's build environment.
Update Project CommandTool to update an existing SSH command in a project.
Update Config FileTool to update an existing config file in a DeployHQ project.
Update Excluded FileTool to update an existing excluded file rule in a project.
Update Project RepositoryTool to update repository configuration for a project in DeployHQ.
Update Server GroupTool to update an existing server group in a DeployHQ project.
Update TemplateTool to update an existing template in DeployHQ.
Create ProjectTool to create a new project in DeployHQ.
Generate AI Deployment OverviewTool to generate an AI-powered deployment overview for a revision range.
Create Build Cache FileTool to create a new build cached file within a project.
Create Build CommandTool to create a new build command for a project in DeployHQ.
Create Project Build Known HostTool to create a new known host in a project using DeployHQ API.
Create SSH CommandTool to create a new SSH command for a project in DeployHQ.
Create Config FileTool to create a new config file in a DeployHQ project.
Create Config File DeploymentTool to create a new config file deployment for a project.
Create Excluded FileTool to add a new excluded file to a project.
Abort DeploymentTool to abort a currently running deployment.
Add Project RepositoryTool to add repository details to a project in DeployHQ.
Create Server GroupTool to create a new server group for automated deployments in a DeployHQ project.
Create ServerTool to create a new server configuration in a DeployHQ project.
Create TemplateTool to create a new template in DeployHQ.
Update Project SettingsTool to update settings of an existing DeployHQ project.
Edit Build Cache FileTool to edit an existing build cache file within a project.
Edit Build CommandTool to edit an existing build command within a template in DeployHQ.
Edit SSH CommandTool to edit an existing SSH command in a DeployHQ project.
Edit Config FileTool to edit an existing config file within a project.
Edit Excluded FileTool to edit an existing excluded file rule within a project.
Update Excluded FileTool to update an existing excluded file rule in a project.
Update Project RepositoryTool to update repository details for an existing project in DeployHQ.
Update Server GroupTool to update a server group in a DeployHQ project using the API.
Edit TemplateTool to edit an existing template in DeployHQ.

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 DeployHQ account and project
  • Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and DeployHQ

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 DeployHQ 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 deployhq_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=["deployhq"],
    )

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

Run the agent

npx ts-node llamaindex-agent.ts

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

Complete Code

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

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

OpenAI Agents SDK

OpenAI Agents SDK

Use DeployHQ MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use DeployHQ MCP with Claude Agent SDK

Google ADK

Google ADK

Use DeployHQ MCP with Google ADK

LangChain

LangChain

Use DeployHQ MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use DeployHQ MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use DeployHQ MCP with Mastra AI

CrewAI

CrewAI

Use DeployHQ MCP with CrewAI

Claude Code

Claude Code

Use DeployHQ MCP with Claude Code

Codex

Codex

Use DeployHQ MCP with Codex

FAQ

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

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

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

Yes, absolutely. You can configure which DeployHQ 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 DeployHQ 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

Never worry about agent reliability

We handle tool reliability, observability, and security so you never have to second-guess an agent action.

Book a call↗Get started for free