How to Use the Writer (AI Enterprise LLM) MCP in Mastra AI

Q: Can Mastra AI use Writer (AI Enterprise LLM) for structured data tasks?

Yes. You can program the workflow engine to creategraph and then populate it using addfiletograph. This makes the information available for retrieval via tools like askquestion.

Q: Does Writer (AI Enterprise LLM) support multilingual content generation for Mastra AI?

It does. The translatetext tool handles translation between supported languages, letting your workflow process and output localized content reliably.

Q: What are the file management tools available in the MCP Server for Mastra AI?

The server provides comprehensive control: listfiles retrieves all files, getfile grabs metadata, and you can use deletefile to permanently remove data.

Build resilient, multi-step workflows with Mastra AI using the Writer (AI Enterprise LLM) MCP Server.

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Connect Writer (AI Enterprise LLM) MCP to Mastra AI

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Setup Writer (AI Enterprise LLM) with Mastra AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Executing Complex Workflows with MCP Server

The core of your workflow engine is running operations. Use `chat_completion` for general text generation, but also rely on specialized tasks like calling `web_search`. This lets the agent gather external data and then use it to inform its final output.

Handling Structured Data with Mastra AI

When your workflow needs reliable information retrieval, you'll manage Knowledge Graphs. First, upload files via `upload_file`, then establish the graph using `create_graph`. The agent can then query it precisely with `ask_question`.

Processing Vision and Documents in Mastra AI

Don't just process text. You can give your workflow visual context by calling `analyze_vision`, which analyzes images or documents based on a specific prompt. For PDFs, the deprecated but available `parse_pdf` converts them to machine-readable formats.

Setup guide

Set up Writer (AI Enterprise LLM) 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 Writer (AI Enterprise LLM) 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: "writer-ai-enterprise-llm-mcp-client",
  servers: {
    "writer-ai-enterprise-llm-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent Writer (AI Enterprise LLM) transactions"
);
console.log(result.text);

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Common questions about Writer (AI Enterprise LLM) MCP in Mastra AI

Yes. You can program the workflow engine to `create_graph` and then populate it using `add_file_to_graph`. This makes the information available for retrieval via tools like `ask_question`.

The underlying MCP Server supports robust failure handling. For application jobs, you can use `list_application_jobs` to check status and `retry_application_job` if a step fails.

It does. The `translate_text` tool handles translation between supported languages, letting your workflow process and output localized content reliably.

The server provides comprehensive control: `list_files` retrieves all files, `get_file` grabs metadata, and you can use `delete_file` to permanently remove data.

The server manages file content and structured data, specifically handling binary contents uploaded for Knowledge Graphs or Vision tasks.

Use it with your favorite AI tools

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