How to Use the Figshare MCP in Mastra AI

Q: How do I search Figshare records in Mastra AI?

You use the searcharticles or searchprojects tool within your agent's instruction set. The results are passed back into the workflow for further analysis.

Build resilient research workflows in Mastra AI by automating Figshare article lifecycle management with built-in retry logic.

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Connect Figshare MCP to Mastra AI

Create your Vinkius account to connect Figshare 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 Figshare with Mastra AI

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Perplexity

Branching workflows with Mastra AI

Create complex logic flows that react to API state changes. If `initiate_file_upload` fails due to a network blip, the framework automatically retries the operation before notifying your team. This is essential for large dataset transfers where stability matters more than raw speed. You define the rules, and the agent handles the execution.

Multi-step article orchestration

Automate the transition of research from raw files to published articles. Your agent can chain `create_private_article`, `update_article`, and `complete_file_upload` in a single workflow. It eliminates the manual steps that usually lead to metadata errors. You get a clean, repeatable process for every submission your team handles.

Conditional data project management

Use `search_projects` to check for existing containers before creating new ones. This prevents duplicate entries and keeps your repository organized. You can set up branches that trigger different actions based on project status. It ensures your data lifecycle remains orderly without constant human oversight.

Setup guide

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

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

const result = await agent.generate(
  "List recent Figshare 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 Figshare. 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 Figshare MCP in Mastra AI

The Mastra agent framework treats the server tools as native capabilities. You simply import the MCP client and spread the tools into your agent definition.

Absolutely, the framework supports the required transport protocols. You define your workflow steps and the agent calls the appropriate article management tools when triggered.

The framework includes built-in exponential backoff for tool calls. If a request to the repository hits a rate limit, the system waits and tries again automatically.

You use the `search_articles` or `search_projects` tool within your agent's instruction set. The results are passed back into the workflow for further analysis.

Security is handled at the transport layer using your private endpoint token. The framework ensures that raw file data and metadata are processed only within your defined, secure environment.

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