How to Use the Estimation Prover MCP in Google ADK

Keep your enterprise Google ADK agents grounded in reality with automated project timeline validation.

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Connect Estimation Prover MCP to Google ADK

Create your Vinkius account to connect Estimation Prover 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 Estimation Prover with Google ADK

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Analyzing Scope Breakdowns in Google ADK

The `validate_estimation` tool evaluates scope structures directly within your Gemini-powered agent workflows. It forces the Google ADK to split monolithic enterprise epics into discrete milestones that do not exceed 48 hours of work. Your long-context model can process massive backlogs, but this MCP Server ensures it does not gloss over the details. Every single sub-task gets scrutinized for hidden complexity before the agent writes anything to your database.

Grounding Timelines with BigQuery Precedents

The `validate_estimation` tool requires your agent to query and cite historical project metrics rather than guessing. Using Gemini's huge context window, the agent pulls actual delivery timelines from your past cloud projects to prove its current math. If the agent attempts to propose a timeline that contradicts your historical velocity, the tool blocks the execution. This mechanism prevents optimistic bias from corrupting your enterprise planning metrics.

Automated Buffer Calculations for Cloud Migrations

The `validate_estimation` tool applies a strict multiplier to any task involving novel cloud infrastructure or third-party APIs. It automatically injects a 40% to 60% contingency buffer to account for integration friction. This logic runs instantly during the planning phase of your Google ADK agent. It keeps your engineering roadmaps realistic by converting gut-feel predictions into mathematically sound timelines.

Setup guide

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

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Common questions about Estimation Prover MCP in Google ADK

Initialize the toolset with the HTTP parameters pointing to your Vinkius MCP Server endpoint. Pass this toolset directly into your agent tool configuration to expose the validation capabilities.

Yes, your agent can read historical velocity metrics from BigQuery and pass them to the tool. This setup allows the MCP Server to validate current estimates against real enterprise history.

They can. The Gemini model uses its long-context window to ingest entire project specs, then calls the MCP Server to systematically break down and buffer the timeline.

The logic checks the novelty score of the task. Familiar tasks get a flat 20% buffer, while complex, unproven integrations trigger a 40% to 60% timeline multiplier automatically.

Your scope breakdowns and risk multipliers are processed within a secure, ephemeral V8 sandbox. Vinkius handles the authentication token securely, ensuring your raw project metrics never touch external servers.

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