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.

Coda MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Coda
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Coda through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Coda "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Coda?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Coda MCP Server

Connect your AI to Coda, the collaborative document platform that brings together words, data, and teams.

Pydantic AI validates every Coda tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Document Browsing — List your recent docs and navigate their sections, tables, and pages.
Table Data — Read rows from any Coda table, filter by column values, and update records.
Formula Values — Retrieve the live value of any named formula in a doc for real-time reporting.

The Coda MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Coda to Pydantic AI via MCP

Follow these steps to integrate the Coda MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Coda with type-safe schemas

Why Use Pydantic AI with the Coda MCP Server

Pydantic AI provides unique advantages when paired with Coda through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Coda integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Coda connection logic from agent behavior for testable, maintainable code

Coda + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Coda MCP Server delivers measurable value.

Type-safe data pipelines: query Coda with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Coda tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Coda and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Coda responses and write comprehensive agent tests

Coda MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Coda to Pydantic AI via MCP:

delete_rows

Delete one or more rows from a Coda table

get_doc_details

Retrieve detailed information about a specific Coda document

get_formula_value

Retrieve the current calculated value of a named formula

insert_rows

Insert new rows into a Coda table

list_columns

Retrieve a list of columns in a Coda table

list_docs

Retrieve a list of Coda documents available to you

list_formulas

Retrieve a list of named formulas in a Coda document

list_rows

Retrieve rows from a specific table in a Coda document

list_tables

Retrieve a list of tables within a specific Coda document

update_row

Update an existing row in a Coda table

Example Prompts for Coda in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Coda immediately.

"Show me my recent documents in Coda."

"Get the current value of formula 'TotalBudget' in doc 'doc-yyyy'."

"Check the status of task 'Q3 Launch' in our Sprint Board table."

Troubleshooting Coda MCP Server with Pydantic AI

Common issues when connecting Coda to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Coda + Pydantic AI FAQ

Common questions about integrating Coda MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Coda MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.