How to Use the Transifex MCP in Mastra AI

Q: Can I list all projects using Transifex and Mastra AI?

Yes. First, call listorganizations. Then, you can use that ID when calling listprojects to filter down the available work areas. This is key for scoping your automated workflows correctly.

Q: How do I get a specific resource by ID using Transifex and Mastra AI?

You use getresource. You must provide the full context: org slug, project slug, and resource slug. The tool then returns the needed data point for your workflow to continue.

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Setup Transifex with Mastra AI

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Check Language Availability in Mastra AI

Need to know which languages are supported globally? Call `list_languages` and you get the full list. Then, use `get_language` with an ID like 'en' or 'pt_BR' if your workflow needs specific details. This helps Mastra AI build conditional steps—if language X is missing, run retry step Y.

Organize and Fetch Projects via Transifex MCP Server

The server lets you map out your entire corporate structure. Start by listing all organizations with `list_organizations`. From there, use `list_projects` to scope down the relevant projects. Later, you can pass a project slug to `get_project` or get a specific resource using `get_resource`, ensuring every step in your workflow has the right context.

Locate Resource Strings with Mastra AI

If localization is part of a complex payment flow, you need source strings. Call `list_resources` to find available resources for a project. Then, use `list_resource_strings` and provide the resource ID. This gives your Mastra workflow the critical text data it needs to process or validate records.

Setup guide

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

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

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

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Common questions about Transifex MCP in Mastra AI

Mastra AI treats the MCP Server tools as callable steps in a workflow. If a step fails—say, fetching project data—the built-in retry logic can try calling `get_project` again. It makes complex, multi-step processes resilient to failure.

Yes. First, call `list_organizations`. Then, you can use that ID when calling `list_projects` to filter down the available work areas. This is key for scoping your automated workflows correctly.

The server manages localization metadata and source string content. When you call `list_resource_strings`, it provides the actual textual content that needs translation. This text is what your workflow operates on.

Absolutely. Since Mastra handles conditional branching and automatic retries, it can call tools like `get_resource` multiple times until the required data is successfully retrieved. It's perfect for high-stakes automation.

You use `get_resource`. You must provide the full context: org slug, project slug, and resource slug. The tool then returns the needed data point for your workflow to continue.

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