How to integrate Simplekpi MCP with LlamaIndex

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Introduction

This guide walks you through connecting Simplekpi to LlamaIndex using the Composio tool router. By the end, you'll have a working Simplekpi agent that can show me top performing kpis this month, add a new kpi for sales pipeline, generate a report on marketing metrics through natural language commands.

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

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

Supported Tools & Triggers

Tools
Add User Group ItemTool to assign a group item to a user in SimpleKPI.
Add User KPITool to assign a KPI to a user in SimpleKPI.
Create Category KPITool to create a new KPI within a category in SimpleKPI.
Create GroupTool to create a new group in SimpleKPI.
Create Group ItemTool to create a new item within a group in SimpleKPI.
Create KPITool to create a new KPI with specified configuration.
Create KPI CategoryTool to create a new KPI category in SimpleKPI.
Create KPI UnitTool to create a new KPI unit in SimpleKPI.
Batch KPI EntriesTool to batch create or update multiple KPI entries at once.
Create UserTool to create a new user account in SimpleKPI.
Delete Category KPITool to delete a KPI from a category.
Delete GroupTool to delete a group by ID.
Delete Group ItemTool to delete a group item by ID.
Delete KPITool to delete a KPI by ID.
Delete KPI CategoryTool to delete a KPI category by its ID.
Delete KPI EntryTool to delete a KPI entry by ID.
Delete KPI UnitTool to delete a KPI unit by its ID.
Delete UserTool to delete a user account by ID.
Delete User Group ItemTool to remove a group item assignment from a user.
Delete User KPITool to remove a KPI assignment from a user.
Get All Data EntriesTool to retrieve processed KPI data entries for reports including calculated KPIs.
Get Category KPITool to retrieve a specific KPI within a category.
Get GroupTool to get a specific group by ID from SimplekPI.
Get Group ItemTool to retrieve a specific group item by ID.
Get KPI by IDTool to retrieve a specific KPI by ID from SimpleKPI.
Get KPI CategoryTool to get a specific KPI category by ID from SimpleKPI.
Get KPI EntryTool to retrieve a specific KPI entry by ID.
Get KPI FrequencyTool to get a specific KPI frequency by ID from SimplekPI.
Get KPI Icon by IDTool to retrieve a specific KPI icon by ID.
Get KPI UnitTool to get a specific KPI unit by ID from SimpleKPI.
Get User by IDTool to retrieve a specific user by ID.
Get User Group ItemTool to get a specific group item assigned to a user.
Get User KPITool to retrieve a specific KPI assigned to a user.
List Category KPIsTool to retrieve all KPIs within a specific category.
List Group ItemsTool to get all items within a group.
List GroupsTool to retrieve all groups from SimpleKPI.
List KPI CategoriesTool to get all KPI categories.
List KPI EntriesTool to get all KPI entries filtered by date range and optional criteria.
List KPI FrequenciesTool to get all KPI frequencies.
List KPI IconsTool to retrieve all KPI icons from SimpleKPI.
List All KPIsTool to retrieve all KPIs from a SimpleKPI account.
List All KPI UnitsTool to retrieve all KPI units from a SimpleKPI account.
List User Group ItemsTool to get all group items assigned to a user.
List User KPIsTool to get all KPIs assigned to a specific user.
Update Category KPITool to update a KPI within a category.
Update GroupTool to update an existing group in SimpleKPI.
Update Group ItemTool to update an existing item in a SimpleKPI group.
Update KPITool to update an existing KPI in SimpleKPI.
Update KPI EntryTool to update an existing KPI entry in SimpleKPI.
Update KPI UnitTool to update an existing KPI unit in SimpleKPI.
Update UserTool to update an existing user account in SimpleKPI.

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

Getting API Keys for OpenAI, Composio, and Simplekpi

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 Simplekpi 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 simplekpi_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=["simplekpi"],
    )

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

Run the agent

npx ts-node llamaindex-agent.ts

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

Complete Code

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

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

OpenAI Agents SDK

OpenAI Agents SDK

Use Simplekpi MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Simplekpi MCP with Claude Agent SDK

Google ADK

Google ADK

Use Simplekpi MCP with Google ADK

LangChain

LangChain

Use Simplekpi MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Simplekpi MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Simplekpi MCP with Mastra AI

CrewAI

CrewAI

Use Simplekpi MCP with CrewAI

Claude Code

Claude Code

Use Simplekpi MCP with Claude Code

Codex

Codex

Use Simplekpi MCP with Codex

FAQ

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

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

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

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

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