How to integrate Coda MCP with LlamaIndex

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Introduction

This guide walks you through connecting Coda to LlamaIndex using the Composio tool router. By the end, you'll have a working Coda agent that can duplicate my project tracker document, add a new permission for this doc, export the content of the roadmap page, create a new page in q2 planning doc through natural language commands.

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

The Coda MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Coda account. It provides structured and secure access to your workspaces and docs, so your agent can automate Coda document creation, manage permissions, export content, and streamline your team’s productivity tools—all on your behalf.

  • Automated document and page creation: Instruct your agent to create new Coda documents or pages, duplicate existing docs, and organize content structure with just a prompt.
  • Flexible permission and sharing management: Have your agent add or modify user, workspace, or global permissions, so you’re always in control of who can view or edit your docs.
  • Seamless content export and status tracking: Let your agent initiate exports of Coda pages and check the progress, making it easy to share or archive important information.
  • Custom domain and publishing management: Direct your agent to add custom domains to published docs or manage categories and makers for Coda packs, keeping your workspace organized and discoverable.
  • Pack release and workspace enhancements: Ask your agent to create new pack releases or manage integrations, helping teams extend Coda’s power without repetitive manual steps.

Supported Tools & Triggers

Tools
Triggers
Add a category for packAdd a publishing category for a given pack.
Add a maker for packSet a maker for a given pack.
Add a permission for packCreate or modify user, workspace, or global permissions for a given pack.
Add custom domainAdd a custom domain to a published doc.
Add permissionAdds a new permission to the doc.
Begin content exportInitiate an export of content for the given page.
Content export statusCheck the status of a page content export
Copy DocumentCreates a copy of an existing coda document.
Create a new pack releaseCreates a new pack release based on an existing pack version.
Create a pageCreate a new page in a doc.
Create docCreates a new coda doc, optionally copying an existing doc.
Create packCreates a new pack, essentially registering a new pack id.
Delete a category for packDelete a publishing category for a given pack.
Delete a maker for packDelete a maker for a given pack, who will not be displayed in the corresponding packs page.
Delete a pageDeletes the specified page.
Delete a permission for packDelete user, workspace, or global permissions for a given pack.
Delete docDeletes a doc.
Delete multiple rowsDeletes the specified rows from the table or view.
Delete packDelete a given pack.
Delete permissionDeletes an existing permission.
Delete rowDeletes the specified row from the table or view.
Deletes a custom domainDeletes a custom domain from a published doc.
Fetch grouped logs by pack org root ingestion idRetrieve the grouped logs of a pack for debugging purpose.
Fetch ingestion executions for packRetrieve the ingestion execution ids of a root ingestion for debugging purpose.
Get acl settingsReturns settings associated with acls for this coda doc.
Get a columnReturns details about a column in a table.
Get a controlReturns info on a control.
Get a formulaReturns info on a formula.
Get analytics last updated dayReturns days based on pacific standard time when analytics were last updated.
Get a pageReturns details about a page.
Get a rowReturns details about a row in a table.
Get a single packReturns a single pack.
Get a tableReturns details about a specific table or view.
Get detailed listing information for a packGet detailed listing information for a pack.
Get doc analytics summaryReturns summarized analytics data for available docs.
Get doc categoriesGets all available doc categories.
Get info about a docReturns metadata for the specified doc.
Get mutation statusGet the status for an asynchronous mutation to know whether or not it has been completed.
Get pack analytics summaryReturns summarized analytics data for packs the user can edit.
Gets custom doc domains providersGets the provider (ie.
Get sharing metadataReturns metadata associated with sharing for this coda doc.
Gets the json schema for pack configurationReturns a json schema applicable for customizing the pack using pack configurations.
Get the difference between two pack versionsGets information about the difference between the specified previous version and next version of a pack.
Get the next valid version for a packGet the next valid version based on the proposed metadata.
Get the source code for a pack versionGet temporary links used to download the source code for the given packid and version
Get user infoReturns basic info about the current user.
List available docsReturns a list of coda docs accessible by the user, and which they have opened at least once.
List categories for packList publishing categories for a given pack.
List columnsReturns a list of columns in a table.
List controlsReturns a list of controls in a coda doc.
List custom doc domainsList all custom domains for a published doc.
List doc analyticsReturns analytics data for available docs per day.
List featured docs for a packReturns a list of featured doc ids for a pack.
List formulasReturns a list of named formulas in a coda doc.
List makers for packList makers for a given pack.
List pack analyticsReturns analytics data for packs the user can edit.
List pack formula analyticsReturns analytics data for pack formulas.
List packsGet the list of accessible packs.
List page analyticsReturns analytics data for a given doc within the day.
List pagesReturns a list of pages in a coda doc.
List permissionsReturns a list of permissions for this coda doc.
List permissions for a packGet user, workspace, and/or global permissions for a given pack.
List table rowsRetrieves rows from a specific table within a coda document.
List tablesReturns a list of tables in a coda doc.
List the pack listings accessible to a userGet listings of public packs and packs created by you.
List the releases for a packGet the list of releases of a pack.
List the versions for a packGet the list of versions of a pack.
List workspace rolesReturns a list of the counts of users over time by role for the workspace.
List workspace usersReturns a list of members in the given workspace.
Pack asset upload completeNote the completion of the upload of a pack asset.
Pack source code upload completeNote the completion of the upload of a pack source code.
Pack version upload completeNote the completion of the upload of a pack version bundle in order to create that pack version.
Patch the system connection credentials of the packPatch the system connection credentials of the pack.
Publish docUpdate publish settings for a doc.
Push a buttonPushes a button on a row in a table.
Register pack versionRegisters a new pack version.
Resolve browser linkGiven a browser link to a coda object, attempts to find it and return metadata that can be used to get more info on it.
Retrieve the grouped logs of a packRetrieve the grouped logs of a pack for debugging purpose.
Retrieve the information for a specific logRetrieve the ingestion execution ids of a root ingestion for debugging purpose.
Retrieve the logs of a ingestionRetrieve the logs of a ingestion for debugging purpose.
Retrieve the logs of a packRetrieve the logs of a pack for debugging purpose.
Retrieve the oauth configuration of the packRetrieve the oauth configuration of the pack for display purpose.
Retrieve the system connection metadata of the packRetrieve the system connection metadata of the pack.
Search Coda DocumentsThis tool allows users to search for coda documents based on a query term.
Search principalsSearches for user and group principals matching the query that this doc can be shared with.
Search Table RowsAction to search for rows in a coda table based on specific criteria.
Set the oauth configurations of the packSet the oauth configurations of the pack, including client id and secret.
Set the system connection credentials of the packSet the system connection credentials of the pack.
Trigger automationTriggers webhook-invoked automation
Unpublish docUnpublishes a doc.
Update acl settingsUpdate settings associated with acls for this coda doc.
Update an existing pack releaseUpdate details of a pack release.
Update a pageUpdate properties for a page.
Update docUpdates metadata for a doc.
Update featured docs for a packCreate or replace the featured docs for a pack.
Update packUpdate an existing pack for non-versioned fields.
Updates a custom domainUpdates properties of a document's custom domain.
Updates user roleUpdates the workspace user role of a user that matches the parameters.
Upload a pack assetRequest a signed s3 url to upload your pack asset.
Upload pack source codeRequest a signed s3 url to upload your pack source code.
Insert/Update Rows in Coda TableThis tool allows you to insert new rows into a coda table or update existing ones based on specified key columns.

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

Getting API Keys for OpenAI, Composio, and Coda

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 Coda 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 coda_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=["coda"],
    )

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

Run the agent

npx ts-node llamaindex-agent.ts

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

Complete Code

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

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

OpenAI Agents SDK

OpenAI Agents SDK

Use Coda MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Coda MCP with Claude Agent SDK

Google ADK

Google ADK

Use Coda MCP with Google ADK

LangChain

LangChain

Use Coda MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Coda MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Coda MCP with Mastra AI

CrewAI

CrewAI

Use Coda MCP with CrewAI

FAQ

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

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

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

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

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