How to Use the Gradient AI (LLM API & Finetuning) MCP in Mastra AI

Q: Can Mastra AI handle tool approval for Gradient AI (LLM API & Finetuning)?

Yes. You can enable requireToolApproval in your client config. This forces the agent to pause until a human confirms actions like model deletion.

Q: How do I list models in Gradient AI (LLM API & Finetuning) using Mastra AI?

Call listmodels as an agent tool. You will get a JSON response containing all base and fine-tuned models available in your account.

Orchestrate complex AI workflows and model training tasks using Mastra AI and this MCP server.

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Connect Gradient AI (LLM API & Finetuning) MCP to Mastra AI

Create your Vinkius account to connect Gradient AI (LLM API & Finetuning) 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 Gradient AI (LLM API & Finetuning) with Mastra AI

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Conditional model workflows in Mastra AI

Build agents that decide when to trigger `fine_tune_model` based on performance metrics. If a model fails a test via `complete_model`, your agent can automatically retry or flag the issue. This gives you programmatic control over your AI infrastructure. You define the logic in your Mastra workflows, and the server executes the heavy lifting.

Multi-step document processing

Chain together `extract_pdf`, `summarize_document`, and `analyze_sentiment` to build robust data pipelines. Your agents handle the flow, ensuring every document is processed correctly. Errors trigger built-in retries. This ensures that even if one step in your Mastra AI pipeline encounters a hiccup, the entire process recovers without manual intervention.

Lifecycle management for custom models

Use `list_models` and `delete_model` to keep your workspace clean. Your agents can prune old or underperforming models based on your specific criteria. This keeps your deployment environment efficient. You maintain full control over which models are active, preventing unnecessary compute costs.

Setup guide

Set up Gradient AI (LLM API & Finetuning) 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 Gradient AI (LLM API & Finetuning) 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: "gradient-ai-llm-api-finetuning-mcp-client",
  servers: {
    "gradient-ai-llm-api-finetuning-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent Gradient AI (LLM API & Finetuning) transactions"
);
console.log(result.text);

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Common questions about Gradient AI (LLM API & Finetuning) MCP in Mastra AI

Add the server URL to your Mastra client configuration. Once connected, your agents can call any of the 19 available tools as part of their workflow steps.

Yes. You can enable `requireToolApproval` in your client config. This forces the agent to pause until a human confirms actions like model deletion.

Mastra provides built-in retry logic for all tools. If a tool call fails, the framework attempts the operation again according to your specified backoff strategy.

Call `list_models` as an agent tool. You will get a JSON response containing all base and fine-tuned models available in your account.

Your training data and RAG collections are scoped to your unique endpoint token. The server uses ephemeral storage to ensure no cross-contamination between agent sessions.

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