How to Use the Fibery MCP in Mastra AI

Q: How do I require approval for Fibery changes?

Use the requireToolApproval setting in your agent. This forces the agent to pause before calling tools like deleteentity until you verify the action.

Build resilient Fibery automation workflows in Mastra AI with automatic retries and conditional branching.

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Connect Fibery MCP to Mastra AI

Create your Vinkius account to connect Fibery 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 Fibery with Mastra AI

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Claude

Perplexity

Automated Workflows with Mastra AI

Chain `create_entity` and `update_entity` into complex agent sequences. If a step fails, the framework handles the retry logic so your data stays consistent. It turns manual tracking into an autonomous loop. You define the logic, and the agent executes the sequence across your databases.

Safe Entity Lifecycle Control

Use `delete_entity` and `query_entities` with human-in-the-loop approvals. You prevent accidental deletions by requiring a signature before the tool executes. This adds a layer of safety to your automated operations. You maintain full visibility into every action the agent takes.

Workspace Architecture Mapping

Call `list_apps` and `get_schema` to let your agent discover the environment. It builds an internal map of your workspace structure before executing any logic. This ensures the agent understands your data relationships. You save time on manual setup by letting the agent query the workspace layout itself.

Setup guide

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

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Fibery. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Fibery MCP in Mastra AI

You register the tools in your agent definition. Once added, the agent uses `create_entity` or `update_entity` based on the conditions you set in your workflow.

You can configure exponential backoff in your agent settings. This prevents the server from hitting API limits when processing large batches of records.

The server uses token-based auth to restrict access. Your sensitive task and project data remains isolated within your authenticated session.

Use the `requireToolApproval` setting in your agent. This forces the agent to pause before calling tools like `delete_entity` until you verify the action.

The server queries entities in chunks. You should implement pagination in your workflow logic if you expect to process over 1,000 records at once.

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