How to Use the Coda MCP in Google ADK

Q: Can Google ADK run Coda operations in parallel?

Yes. Gemini models can invoke multiple tools, like running listformulas and listdocs simultaneously, to gather context faster during complex reasoning tasks.

Connect Gemini's 1M token context to your Coda workspaces with the Google ADK.

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Connect Coda MCP to Google ADK

Create your Vinkius account to connect Coda to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Coda with Google ADK

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Perplexity

Analyze giant documents with Google ADK

Gemini models thrive on huge context windows. By using `get_doc_details` and `list_rows`, you can feed entire Coda tables directly into your agent's memory to find patterns across thousands of records. This lets you ask complex questions about your team's historical data. The Google ADK handles the transport, making it easy to parse massive text blocks without hitting token limits.

Write analytical insights back to your MCP Server

Run your heavy SQL queries in BigQuery, then have your agent update your team's tracker. The agent uses `insert_rows` or `update_row` to push clean, parsed results right into Coda. This bridges your enterprise data warehouse and your team's daily workspace. You don't have to build custom ETL pipelines; the agent does the data entry for you.

Restrict Google ADK tool access

You might not want an automated agent deleting your files. The MCP Server lets you use a tool_names filter to expose only safe tools like `list_tables` and `get_formula_value` while hiding destructive ones. This keeps your enterprise agent boxed in. It can read the status of your projects without any risk of calling `delete_rows` by mistake.

Setup guide

Set up Coda MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Coda tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Coda_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Coda tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Coda MCP in Google ADK

Use the McpToolset class with your Vinkius HTTP endpoint. Pass that toolset to your LlmAgent to instantly give your Gemini models access to your Coda workspace.

Yes. Gemini models can invoke multiple tools, like running `list_formulas` and `list_docs` simultaneously, to gather context faster during complex reasoning tasks.

Yes. You can run this MCP Server locally using Stdio during development, then switch to our hosted HTTP transport when deploying your agent to Google Cloud.

You can restrict access at the API token level in Coda, or use the ADK's tool filtering to prevent the agent from calling `list_tables` on unauthorized documents.

Your Coda document text and table rows are processed inside isolated, ephemeral environments. No Coda data is ever cached or used for training models on Google Cloud.

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