How to Use the LinearB MCP in Mastra AI

Q: Can Mastra AI automate LinearB incident reporting?

Absolutely. You can set up a conditional branch that calls recordnewincident if a deployment health check fails, ensuring your records stay accurate.

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Connect LinearB MCP to Mastra AI

Create your Vinkius account to connect LinearB 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 LinearB with Mastra AI

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Automated incident response logic

Use `record_new_incident` to trigger automated workflows the moment a failure is detected in your CI/CD pipeline. You can chain this with other Mastra AI tools to notify on-call engineers or rollback faulty deployments. The logic is deterministic and reliable. By using the MCP server, you ensure that every incident is logged with the correct timestamp and provider ID without human intervention.

Conditional delivery analysis

Configure your agents to run `list_software_deployments` and `query_software_metrics` as part of a decision tree. If metrics drop below a certain threshold, the agent can stop further deployments automatically. This builds a safety net around your release process. Mastra AI handles the retries and branching, while the MCP tools provide the ground truth for every decision.

Repository and team management

Manage your scope using `list_connected_repos` and `list_engineering_teams` to ensure agents only act on relevant data. This prevents your workflows from being overwhelmed by irrelevant noise from inactive projects. Agents can now distinguish between critical production teams and experimental test groups. It keeps your automation focused on the repositories that actually move the needle.

Setup guide

Set up LinearB 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 LinearB 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: "linearb-mcp-client",
  servers: {
    "linearb-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

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

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Common questions about LinearB MCP in Mastra AI

Add the LinearB MCP server to your Mastra agent configuration. You then map the tools like `query_software_metrics` directly into your workflow steps.

Absolutely. You can set up a conditional branch that calls `record_new_incident` if a deployment health check fails, ensuring your records stay accurate.

The framework's built-in retry logic handles transient network errors. You can configure exponential backoff for your tool calls to maintain system stability.

You can use Mastra's requireToolApproval setting. This lets you inspect the data returned by `list_software_deployments` before the agent takes any further action.

The server only transmits operational metadata like repository IDs and incident timestamps. Personal identifiers are not sent through the MCP interface.

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