How to Use the Travis CI MCP in Mastra AI

Q: How does Mastra AI handle multi-stage deployment across Travis CI?

Mastra AI manages this by first calling listtravisrepositories to survey all target environments. It then uses the details from getrepositorydetails to ensure credentials and branch structures are correct before proceeding.

Q: If a critical build fails on Travis CI, can Mastra AI automatically retry it?

Yes. If the workflow detects failure, it uses restarttravisbuild. This isn't just a restart; Mastra AI treats it as an intelligent recovery step built into its core logic.

Q: What if I need to check multiple branches on Travis CI using Mastra AI?

You can use listrepositorybranches to get the status of all available branches. This gives Mastra AI a comprehensive view, allowing it to decide which branch is safe for deployment next.

Q: How does Mastra AI monitor user permissions related to Travis CI builds?

Mastra AI uses getuserprofile to verify the identity of the agent executing the task. This ensures that every operation, including build triggering or cancellation, adheres to the established security context.

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Setup Travis CI with Mastra AI

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Survey Project Health

To check the overall state of your organization, call `list_travis_repositories`. This lists every repository configured on Travis CI, giving you a quick overview. You can then follow up with `list_repository_builds` to get recent execution history for any specific project. This capability helps Mastra AI's workflow engine decide which projects need immediate attention or require status checks before moving forward.

Inspect Build Failures

When a build fails, you don’t want to guess why. Use `get_build_details` to pull full run details for any specific Travis CI job. This level of depth allows Mastra AI's workflow engine to intelligently determine if the failure is transient or structural. You can narrow down the issue by using `list_build_jobs`, which provides a list of every individual job within a single, complex build.

Automate Recovery and Testing

If a pipeline step fails, don't panic. Mastra AI can use the MCP Server to `restart_travis_build` using a previously captured build ID. This mechanism allows for automatic retries with exponential backoff built into the workflow. Alternatively, you can explicitly call `trigger_new_build`, providing the necessary repo slug and branch to kick off an immediate diagnostic test.

Setup guide

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

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

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

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Common questions about Travis CI MCP in Mastra AI

Mastra AI manages this by first calling `list_travis_repositories` to survey all target environments. It then uses the details from `get_repository_details` to ensure credentials and branch structures are correct before proceeding.

Yes. If the workflow detects failure, it uses `restart_travis_build`. This isn't just a restart; Mastra AI treats it as an intelligent recovery step built into its core logic.

You can use `list_repository_branches` to get the status of all available branches. This gives Mastra AI a comprehensive view, allowing it to decide which branch is safe for deployment next.

The MCP Server manages critical identifiers like repository slugs and build IDs. These strings allow the workflow engine to precisely target and manipulate specific code repositories.

Mastra AI uses `get_user_profile` to verify the identity of the agent executing the task. This ensures that every operation, including build triggering or cancellation, adheres to the established security context.

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