How to integrate Fireberry MCP with LlamaIndex

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Introduction

This guide walks you through connecting Fireberry to LlamaIndex using the Composio tool router. By the end, you'll have a working Fireberry agent that can add new lead to contacts table, list all open deals in pipeline, fetch picklist options for deal stage through natural language commands.

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

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

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

The Fireberry MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Fireberry CRM account. It provides structured and secure access to your CRM data, so your agent can perform actions like creating records, querying customer information, and managing picklists on your behalf.

  • Automated record creation: Let your agent swiftly add new entries to any Fireberry table, such as contacts, leads, or deals, using structured data you provide.
  • Smart CRM data retrieval: Ask your agent to query records with powerful filtering, sorting, and pagination—perfect for finding the exact customer or deal you need.
  • Picklist value management: Effortlessly fetch all available options for any picklist (dropdown) field, making data entry and workflow automation simpler and error-free.
  • Custom module support: Enable your agent to work with any Fireberry module, so you can handle specialized business processes or custom workflows.

Supported Tools & Triggers

Tools
Create a CompetitorTool to create a new competitor in Fireberry.
Create a Fireberry contactTool to create a new contact in Fireberry CRM.
Create an Activity LogCreate a new Activity Log record in Fireberry.
Create a date fieldTool to create a new date field in a Fireberry object/table.
Create a Fireberry lookup fieldTool to create a lookup field in Fireberry CRM.
Create a new Fireberry accountTool to create a new account in Fireberry CRM.
Create an ArticleTool to create a new article in Fireberry.
Create a Fireberry assetCreates a new asset (account product) in Fireberry.
Create an OpportunityTool to create a new opportunity in Fireberry CRM.
Create an Order ItemTool to create a new order item in Fireberry CRM.
Create a noteCreate a new note record in Fireberry.
Create a phone callCreates a new phone call record in Fireberry's call log.
Create a Fireberry productTool to create a new product in Fireberry.
Create a ProjectTool to create a new project in Fireberry CRM.
Create a TaskTool to create a new task in Fireberry CRM.
Create a TicketTool to create a new ticket (case) in Fireberry CRM.
Create a URL fieldTool to create a new URL field in a Fireberry object/table.
Create a CampaignTool to create a new campaign in Fireberry.
Create a CRM OrderTool to create a new CRM Order in Fireberry.
Upload file to Fireberry recordTool to upload a file to a specific record in Fireberry.
Create a Fireberry meetingTool to create a new meeting (activity) in Fireberry.
Create a new Fireberry recordCreates a new record in a specified Fireberry table/module.
Delete an AccountTool to delete an account from Fireberry using its GUID.
Delete a CompetitorTool to delete a competitor in Fireberry by its GUID.
Delete a contractTool to delete a contract in Fireberry by its GUID.
Delete an Activity LogDelete an activity log by its GUID.
Delete a custom fieldTool to delete a custom field from a Fireberry object/table.
Delete an ArticleTool to delete an article from Fireberry by its GUID.
Delete a Fireberry assetDelete an asset from Fireberry by its unique identifier.
Delete an OpportunityTool to delete an opportunity in Fireberry by its GUID.
Delete an Order ItemTool to delete an order item in Fireberry by its GUID.
Delete a NoteTool to delete a note from Fireberry using its GUID.
Delete a Phone CallTool to delete a phone call record from Fireberry using its GUID.
Delete a projectTool to delete a project from Fireberry using its GUID.
Delete a taskTool to delete a task in Fireberry by its GUID.
Delete a ticketTool to delete a ticket (case) from Fireberry using its GUID.
Delete a Business UnitTool to delete a Business Unit in Fireberry using its GUID.
Delete a Fireberry campaignTool to delete a campaign from Fireberry.
Delete a ContactTool to delete a contact from Fireberry using its GUID.
Delete a CRM UserTool to delete a CRM user from Fireberry using its GUID.
Delete a MeetingTool to delete a meeting activity from Fireberry by its GUID.
Delete a productTool to delete a product in Fireberry by its GUID.
Get an AccountTool to retrieve a specific account record by its GUID.
Get a CRM OrderTool to retrieve a specific CRM Order from Fireberry by its GUID.
Get All AccountsTool to retrieve all accounts from Fireberry CRM with pagination support.
Get All Activity Logs (v2)Tool to retrieve all activity logs from Fireberry using v2 API endpoint with pagination support.
Get all articles from FireberryTool to retrieve all articles from Fireberry with pagination support.
Get All AssetsTool to retrieve all assets (account products) from Fireberry with pagination support.
Get All Business Units (v2)Tool to retrieve all business units from Fireberry using v2 API endpoint with pagination support.
Get All CampaignsTool to retrieve all campaigns from Fireberry with pagination support.
Get All Competitors (v2)Tool to retrieve all competitors from Fireberry with pagination support.
Get All ContactsTool to retrieve all contacts from Fireberry with pagination support.
Get All ContractsTool to retrieve all contracts from Fireberry with pagination support.
Get All Custom Object RecordsTool to retrieve all records from a specified custom object in Fireberry with pagination support.
Get All MeetingsTool to retrieve all meetings (activities) from Fireberry CRM with pagination support.
Get All Notes (Detailed)Tool to retrieve all notes from Fireberry with detailed field schema and pagination support.
Get All ObjectsTool to retrieve all object type metadata from Fireberry.
Get All Order ItemsTool to retrieve all order items from Fireberry with pagination support.
Get All OrdersTool to retrieve all orders from Fireberry with pagination support.
Get All Phone CallsTool to retrieve all phone call records from Fireberry with pagination support.
Get All Projects (v2)Tool to retrieve all projects from Fireberry with pagination support.
Get All TasksTool to retrieve all tasks from Fireberry with pagination support.
Get All TicketsTool to retrieve all ticket records (cases) from Fireberry with pagination support.
Get All UsersTool to retrieve all CRM users from Fireberry with pagination support.
Get a MeetingTool to retrieve a specific meeting/activity record by its unique identifier (GUID).
Get an Activity LogTool to retrieve a specific activity log record from Fireberry by its GUID.
Get an ArticleTool to retrieve a specific article from Fireberry by its GUID.
Get an AssetTool to retrieve a specific asset record by its GUID.
Get an ObjectTool to retrieve metadata for a specific object by its ID.
Get an Object's FieldsTool to retrieve metadata about fields for a specific object type in Fireberry.
Get an OpportunityTool to retrieve a specific opportunity record by its GUID.
Get an Order ItemTool to retrieve a specific order item record by its GUID.
Get a NoteTool to retrieve a specific note record by its GUID.
Get a phone call recordTool to retrieve a specific phone call record from Fireberry by its GUID.
Get a ProductTool to retrieve a specific product record by its GUID.
Get a ProjectTool to retrieve a specific project from Fireberry by its GUID.
Get a TaskTool to retrieve a specific task record by its GUID.
Get a TicketTool to retrieve a specific ticket (case) record by its GUID.
Get Campaign by IDTool to retrieve a single campaign by its GUID.
Get a CompetitorTool to retrieve a specific competitor record by its GUID.
Get a ContactTool to retrieve a specific contact record by its GUID.
Get Custom Object RecordTool to retrieve a specific custom object record by its GUID and object code.
Get Field DetailsTool to retrieve detailed metadata for a specific field in a Fireberry object/table.
Get Object Field ValuesTool to retrieve picklist field values from the metadata endpoint.
Get Items for an OrderTool to retrieve all items for a specific order from Fireberry.
Get Picklist Field ValuesTool to retrieve picklist field values from Fireberry metadata API.
Get Picklist ValuesTool to retrieve all possible picklist (dropdown) values for a specific field by querying records and extracting unique values.
Get Related RecordsTool to retrieve related records for a specific object in Fireberry.
Get Fireberry Task by IDTool to retrieve a single task record by its unique ID (GUID).
Get a Fireberry user by IDTool to retrieve a single user by their unique ID from Fireberry.
List All OpportunitiesTool to retrieve all opportunities from Fireberry CRM with pagination support.
List All ProductsTool to retrieve all products from Fireberry CRM with pagination support.
Fireberry: Query RecordsQuery and retrieve records from a Fireberry module with optional filtering, sorting, and pagination.
Query Fireberry records with filtersQuery records in any Fireberry object with advanced filtering, sorting, and pagination.
Update a Business UnitTool to update an existing business unit in Fireberry.
Update Fireberry AccountUpdates an existing account record in Fireberry with new field values.
Update a Fireberry CompetitorUpdates an existing competitor record in Fireberry by GUID.
Update a Fireberry contactTool to update an existing contact in Fireberry CRM.
Update a ContractTool to update an existing contract in Fireberry.
Update an Activity LogUpdate an existing Activity Log record in Fireberry.
Update a Date FieldTool to update a date field configuration in Fireberry.
Update a Date & Time FieldTool to update a Date & Time field's properties in Fireberry.
Update a Formula FieldTool to update a formula field in Fireberry CRM.
Update an HTML FieldTool to update an HTML field configuration in Fireberry.
Update a Fireberry MeetingTool to update an existing meeting (activity) in Fireberry.
Update a Fireberry articleUpdates an existing article in Fireberry.
Update an AssetUpdate an existing asset (accountproduct) in Fireberry.
Update an Email Address FieldTool to update the configuration of an email address field in Fireberry.
Update an OpportunityTool to update an existing opportunity in Fireberry CRM.
Update an Order ItemTool to update an existing order item in Fireberry.
Update a Number FieldTool to update a number field configuration in Fireberry.
Update a Phone Number FieldTool to update a phone number field configuration in Fireberry.
Update a ProductTool to update an existing product in Fireberry.
Update a ProjectTool to update an existing project in Fireberry CRM.
Update a Text Area FieldTool to update a Text Area field's properties in Fireberry.
Update a Text FieldTool to update a text field configuration in Fireberry.
Update a TicketTool to update an existing ticket (case) in Fireberry.
Update a URL FieldTool to update a URL field configuration in Fireberry.
Update a UserTool to update an existing user in Fireberry CRM.
Update a Fireberry CampaignTool to update an existing campaign in Fireberry by its GUID.
Update a CRM OrderTool to update an existing CRM order in Fireberry.
Update a phone call recordTool to update an existing phone call record in Fireberry.
Update a Task (V2)Tool to update an existing task using Fireberry v2 API.

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

Getting API Keys for OpenAI, Composio, and Fireberry

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 Fireberry 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 fireberry_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=["fireberry"],
    )

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

Run the agent

npx ts-node llamaindex-agent.ts

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

Complete Code

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

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

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FAQ

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

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

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

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

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