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Coda MCP. Manage tables, formulas, and docs via natural language.

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
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Works with every AI agent you already use

…and any MCP-compatible client

Coda MCP on Cursor AI Code Editor MCP Client Coda MCP on Claude Desktop App MCP Integration Coda MCP on OpenAI Agents SDK MCP Compatible Coda MCP on Visual Studio Code MCP Extension Client Coda MCP on GitHub Copilot AI Agent MCP Integration Coda MCP on Google Gemini AI MCP Integration Coda MCP on Lovable AI Development MCP Client Coda MCP on Mistral AI Agents MCP Compatible Coda MCP on Amazon AWS Bedrock MCP Support

Just plug in your AI agents and start using Vinkius.

Coda MCP Server lets your AI agent manage data, documents, and formulas inside Coda. You can list documents, read specific table rows, update records, and get live formula values directly from chat.

It's a full-featured data pipeline for anyone who needs to automate tasks within the collaborative document platform.

What your AI agents can do

Delete rows

Deletes one or more rows from a Coda table.

Get doc details

Retrieves detailed information about a specific Coda document.

Get formula value

Gets the current calculated value of a named formula.

+ 7 more capabilities included
Find Document Details

Retrieves metadata about a specific Coda document, including its tables and pages.

List Available Documents

Provides a list of all Coda documents the agent can access.

Read and Modify Table Data

Lists, reads, inserts, updates, or deletes entire rows from a specified Coda table.

Analyze Document Structure

Lists tables within a document or retrieves a list of available columns in a specific table.

Get Live Formula Values

Reads the current calculated value of any named formula within a document.

Supported MCP Clients

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
+ other MCP clients
Free for Subscribers

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AI Agent

delete019d7575

delete rows

Deletes one or more rows from a Coda table.

get019d7575

get doc details

Retrieves detailed information about a specific Coda document.

get019d7575

get formula value

Gets the current calculated value of a named formula.

insert019d7575

insert rows

Adds new rows of data to a specified Coda table.

list019d7575

list columns

Gets a list of column names available in a Coda table.

list019d7575

list docs

Retrieves a list of all Coda documents the agent can see.

list019d7575

list formulas

Gets a list of named formulas in a Coda document.

list019d7575

list rows

Retrieves the data from specific rows in a Coda table.

list019d7575

list tables

Gets a list of tables contained within a specific Coda document.

update019d7575

update row

Changes the content of an existing row in a Coda table.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

  • Import from OpenAPI, Swagger, or YAML specs
  • Create Agent Skills with progressive disclosure
  • Deploy to edge with MCPFusion framework
  • Built in DLP, auth, and compliance on every call
  • Real time usage dashboard and cost metering
  • Publish to catalog or keep private
Start building

Make Your AI Do More

Start with Coda, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.

  • Use this MCP plus 4,700+ others, all in one place
  • Add new capabilities to your AI anytime you want
  • Every connection is secured and compliant automatically
  • Track usage and costs across all your servers
  • Works with Claude, ChatGPT, Cursor, and more
  • New servers added to the catalog every week

What you can do with this MCP connector

Your AI agent can handle data, docs, and formulas inside Coda. It treats the platform like a data source, not just a word processor. You can list available documents using list_docs, and you can get specific details about any doc with get_doc_details. You'll also be able to see all the tables inside a document with list_tables, or check out the column names for a specific table using list_columns.

How Coda MCP Works

  1. 1 First, tell the agent which document you're interested in (e.g., 'Project Tracker'). Use list_docs to see what's available.
  2. 2 Next, specify the data you need. For example, ask the agent to check the status of a task, which requires running list_rows on a specific table.
  3. 3 The agent retrieves the data, processes it, and gives you the answer without you ever leaving the chat.

The bottom line is, your AI agent treats Coda like a database, letting you manage data and structure through simple conversation.

Who Is Coda MCP For?

This is for Product Managers, Operations Engineers, and Developers who rely on Coda as a central source of truth. If your job involves checking status updates, pulling metrics, or updating trackers without opening the platform, this tool saves hours of context switching.

Product Manager

Pulls current table data and formula results into a report or presentation without having to open and navigate the source Coda doc.

Operations Engineer

Updates status fields or checks tracking progress on critical tables directly from a chat interface, keeping project status centralized.

Developer

Automates data synchronization tasks, using the agent to pull data from Coda and push it to another system.

What Changes When You Connect

  • You read table data and formula results without opening the document. Just ask your agent to 'What is the current budget?' and use get_formula_value to get the answer immediately.
  • Update project statuses from chat. Instead of opening a tracker and manually changing cells, use update_row to change a task's status to 'Complete' and log it.
  • Automate data collection. Use list_docs and list_tables to map out all the data sources in your workspace before writing a script that needs to pull data.
  • Ensure data accuracy. Before updating anything, use list_columns to verify the exact column names, preventing the agent from writing to the wrong field.
  • Handle multiple sources. If you need data from three different documents, the agent can chain calls using list_rows across multiple sources, all from one prompt.
  • Handle missing data. If a row is missing, the agent can use list_rows to check for specific criteria, or use insert_rows if the record needs to be created.

Real-World Use Cases

01

The QBR Prep Task

The Product Manager needs to show the QBR team the latest metrics from the 'Project Tracker' without opening the source document. They prompt the agent: 'What is the total budget and task status for the Q3 launch?' The agent runs list_rows and get_formula_value, pulling the data and presenting a clean, summarized report instantly.

02

The Ops Status Update

The Ops Engineer sees a critical bug ticket and needs to change its status. They tell the agent: 'Change the status of bug ID 452 to 'Needs Review' in the 'Bug Tracker' table.' The agent uses update_row, modifying the record directly and leaving an audit trail.

03

The Data Sync Workflow

A Developer needs to pull all active project IDs from the 'Master List' document to feed into a Python script. They ask the agent to 'Get all IDs from the 'Project ID' column in the 'Master List' table.' The agent runs list_rows and outputs the raw data for the developer to use in their code.

04

The New Initiative Launch

The PM decides to launch a new project. Instead of creating a new doc and manually setting up the tracker, they ask the agent to 'Create a new row for the 'Alpha Project' with the owner set to Jane and status set to 'Pending'.' The agent uses insert_rows to populate the new record.

The Tradeoffs

Manual Data Guessing

A user manually tries to update a row, assuming the column is called 'Completion Date' when the actual name is 'Target Delivery Date'. They run update_row and it fails silently or writes to the wrong field.

Always run list_columns first to confirm the exact column name. Then, use that precise name when calling update_row to guarantee data integrity.

Reading Data in Chunks

Trying to check the status of 50 tasks by calling list_rows multiple times with small filters, leading to slow, incomplete, or rate-limited results.

Use list_rows with comprehensive filters to pull the entire dataset you need in one go. If you only need a few specific values, list_columns can help you narrow your scope before reading.

Ignoring Document Scope

Attempting to run a formula check on a document that hasn't been updated recently, leading to an outdated calculation result.

Always run get_doc_details first to confirm the document's last edit time and overall status before relying on get_formula_value.

When It Fits, When It Doesn't

Use this server if your workflow requires reading, writing, or calculating data within Coda documents. You need the agent to act like a data analyst, not just a reader. Don't use it if your goal is simply to write a document or brainstorm ideas—use Coda's native editor for that. If your primary need is cross-system data sync (e.g., Coda to Salesforce), you'll need a dedicated integration layer outside of this MCP server.

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Coda. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.

Available Capabilities

delete_rows get_doc_details get_formula_value insert_rows list_columns list_docs list_formulas list_rows list_tables update_row

Tired of copy-pasting data between Coda and your spreadsheets.

Right now, if you need to take data from a Coda tracker and put it into a presentation slide, you open Coda, select the rows, copy them, open Google Slides, and paste. You repeat that for every single slide, and the data is almost never perfectly aligned or up-to-date.

With the Coda MCP Server, you ask your agent to pull the exact data you need—like 'all Q3 launch tasks'—and it pulls it directly into the chat. You get clean, structured data output, ready to be pasted anywhere, without opening the source doc.

Coda MCP Server: Manage data and formulas.

Manual work includes opening the document, clicking the table, finding the specific formula, and then manually reading the displayed result. This is slow and requires you to track multiple open tabs just to get one number.

Now, you just ask the agent, 'What is the total budget for Project Alpha?' The agent uses `get_formula_value` and gives you the number immediately. It's faster, and it's guaranteed to be the live value.

Common Questions About Coda MCP

How do I check if a document is ready for data extraction using the Coda MCP Server? +

Use get_doc_details to pull detailed information about the document. This tells you if the document is current and what kind of structure it holds.

Can I update a row in Coda using the `update_row` tool? +

Yes. You must specify the document, the table, the row, and the exact column name you want to change. This makes sure the agent updates the correct record.

Which tool should I use to see all the tables inside a Coda document? +

Use list_tables. This tool retrieves a list of all tables contained within a specific document, helping you map out the data landscape.

How do I get the live value of a formula in Coda using `get_formula_value`? +

You provide the document name and the exact named formula. The tool returns the current calculated value, ensuring your report uses the most up-to-date number.

How do I list all the documents I have using the `list_docs` tool? +

The list_docs tool retrieves a list of all your Coda documents. It shows the document name, how long ago it was edited, and how many tables it contains, helping you narrow down your target data source.

What if I need to delete some data, what is the scope of the `delete_rows` tool? +

The delete_rows tool removes one or more specified rows from a Coda table. You must provide the exact table and row identifiers to ensure only the intended data is deleted.

Can I retrieve data from specific tables using the `list_rows` tool? +

Yes, list_rows lets you pull rows from a specific table within a document. You need to specify the document ID, table name, and row identifiers to get the data you need.

How do I check the columns available in a table using the `list_columns` tool? +

The list_columns tool gives you a list of all columns in a Coda table. This lets you know what data points you can read, filter, or update before running any data operation.

How do I find my doc and table IDs? +

You can find IDs in the document's URL, or use the 'list_docs' and 'list_tables' tools to discover them.

What does 'HTTP 202 Accepted' mean? +

Coda processes many changes asynchronously. This status means your request was queued and will be applied shortly.

Can I trigger buttons in Coda? +

Many Coda buttons are column actions that can be triggered by updating a row value. So yes, indirectly.

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Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
+ other MCP clients

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