How to Use the Tally MCP in Google ADK

Q: How do I ensure Tally submission records are ready for BigQuery with Google ADK?

Use listsubmissions to pull all required audit data. The long-context capabilities of the Gemini models in your agent can then process this large volume of structured text.

Q: What is the best way to get metadata for Tally forms using Google ADK?

You use getform to retrieve the specific details. This information allows the agent to validate and prepare the schema before writing it to a data store.

Q: Does Google ADK support auditing multiple Tally workspaces?

Yes. By combining listworkspaces with subsequent calls to list forms, your agent can systematically map out all available operational scopes within your organization’s data structure.

Q: Can I delete old Tally submissions using Google ADK?

Yes. You call deletesubmission. The agent uses this ability to clean up or archive records, maintaining a manageable dataset for subsequent BigQuery analysis runs.

Connect Tally Data to BigQuery with Google ADK.

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Setup Tally with Google ADK

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Ingest Form Schemas for Analysis

Before feeding data into a data warehouse, you need the blueprint. Use `get_form` to retrieve deep details about any Tally form structure. This metadata is perfect for mapping fields directly into BigQuery tables. Your agent can verify required inputs by checking if the schema provided by `get_form` meets your enterprise standards before running bulk data loads.

Audit Submission Logs

Need to audit what was submitted? Run `list_submissions`. This tool provides a paginated list of submission records, letting your Google ADK agent gather thousands of historical entries for long-context reasoning. The data is ready for deep analysis. Once retrieved, the results can be streamed directly into Vertex AI or BigQuery for immediate pattern recognition.

Manage Form Lifecycles

The `list_forms` tool helps your agent track all existing forms. This is essential when building complex pipelines that need to know which forms are active and available across different workspaces. Your agent can filter this list using a specific workspace ID, keeping the data flow clean and targeted for reliable ingestion.

Setup guide

Set up Tally 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 Tally 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="Tally_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Tally tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Tally MCP in Google ADK

Use `list_submissions` to pull all required audit data. The long-context capabilities of the Gemini models in your agent can then process this large volume of structured text.

You use `get_form` to retrieve the specific details. This information allows the agent to validate and prepare the schema before writing it to a data store.

Yes. By combining `list_workspaces` with subsequent calls to list forms, your agent can systematically map out all available operational scopes within your organization’s data structure.

Yes. You call `delete_submission`. The agent uses this ability to clean up or archive records, maintaining a manageable dataset for subsequent BigQuery analysis runs.

The server exposes detailed submission metadata, including who submitted it and when. This allows your agent to trace specific actions back to users within the Google Cloud infrastructure.

Use it with your favorite AI tools

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