How to Use the Estimation Prover MCP in Mastra AI

Build resilient workflows that refuse to accept optimistic delivery dates. Connect the Estimation Prover to Mastra AI.

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Connect Estimation Prover MCP to Mastra AI

Create your Vinkius account to connect Estimation Prover to Mastra AI 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 Mastra AI

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Claude

Perplexity

Force reality checks in Mastra AI workflows

Workflows break when they rely on bad estimates. The Estimation Prover makes your Mastra AI agent decompose scope into chunks of two days or less. If your agent tries to pass a massive, vague feature through the pipeline, the `validate_estimation` tool rejects it. You can catch these rejections using Mastra's built-in conditional branching. If the tool flags an unrealistic timeline, your workflow automatically routes the task back for a deeper breakdown. It forces the system to do the hard work before moving forward.

Demand proof before proceeding

Optimism is a defect. This MCP Server requires your agent to cite actual historical precedents for its estimates. It cannot just say a task takes three days based on experience. It needs a specific past project to prove the velocity. When the `validate_estimation` tool runs, it evaluates the precedent against the current scope. If the agent fails to provide a match, Mastra's automatic retries kick in. The agent tries again, digging for real data, until it proves the timeline is grounded in reality.

Calculate the worst-case scenario

You have to plan for failure. The tool forces a strict mathematical buffer on every estimate. Familiar tasks get hit with a 20% markup. Anything novel takes a 40% to 60% penalty. We don't negotiate with reality. Your agent must explicitly state the assumptions keeping the timeline intact. Mastra AI takes this output and can pause the workflow, using requireToolApproval to put a human in the loop. You review the massive buffer, approve the reality check, and let the deployment continue.

Setup guide

Set up Estimation Prover MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Estimation Prover tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "estimation-prover-mcp-client",
  servers: {
    "estimation-prover-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Estimation Prover Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Estimation Prover tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Estimation Prover transactions"
);
console.log(result.text);

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Common questions about Estimation Prover MCP in Mastra AI

Install @mastra/mcp@latest. Create a new MCPClient with the server URL, call listTools(), and spread them into your agent's configuration. It automatically handles the HTTP transport.

Your agent is hallucinating a happy path. The tool rejects any estimate lacking a 20% minimum buffer or tasks larger than two days. Use conditional branching to catch the rejection and prompt the agent to fix it.

Yes. Turn on requireToolApproval for the validation step. The workflow will pause, letting you review the forced contingency buffers before the agent finalizes the project plan.

Mastra AI uses exponential backoff. If the server is unreachable for a split second, the framework automatically retries the tool call without breaking your entire pipeline.

We process your task descriptions, precedent citations, and risk maps in a zero-trust, ephemeral environment. The server validates the math and drops the state immediately. We don't log your internal project names.

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