How to Use the Elsevier ScienceDirect MCP in Mastra AI

Q: How can my Mastra AI agent handle access errors from Elsevier ScienceDirect?

Design a workflow where a failed getarticle call triggers a new step. That step can call getarticleentitlement to find out why, allowing your Mastra AI agent to log the specific error and continue.

Q: Can I use Mastra AI to build a daily research brief from Elsevier ScienceDirect?

Yes. A scheduled Mastra AI agent can run searchsciencedirect with your topics, fetch abstracts via getarticle, and assemble a report. The framework's built-in reliability is perfect for this kind of recurring job.

Q: How does my agent look up journal data using an ISSN?

Your agent should use the getserialtitle tool. Provide the ISSN, and it returns structured metadata about that specific journal, which is great for verifying sources within a workflow.

Build resilient research workflows on Elsevier ScienceDirect with Mastra AI's automatic retries and conditional logic.

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Connect Elsevier ScienceDirect MCP to Mastra AI

Create your Vinkius account to connect Elsevier ScienceDirect 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 Elsevier ScienceDirect with Mastra AI

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Create Fault-Tolerant Research Bots

APIs fail. Mastra AI is built for that reality. Your agent can attempt to `get_article`, and if the network flakes out, Mastra's engine automatically retries with exponential backoff. This is essential for long-running data ingestion jobs that can't afford to stop halfway through. Don't just retry—react. You can design workflows with conditional branches. For instance: if a call to `get_article` fails with an auth error, your agent can automatically run `get_article_entitlement` to check permissions, log the issue, and then move to the next item in the queue. You're building a robust process, not just a simple script.

Automate Content Ingestion Pipelines

Set up an agent to run `search_sciencedirect` and find a batch of articles. Then, have it loop through each result, calling `get_article_metadata` for citation info and `get_article` to pull the abstract. This becomes a reliable, automated pipeline for your knowledge base. The workflow won't crash if one article fails. Mastra AI's engine isolates failures, allowing the agent to continue processing the rest of the batch. You can even add a step that uses `get_subject_classifications` to automatically tag each article before inserting it into your database.

Build Workflows with Human-in-the-Loop

Some decisions need a person. Mastra AI lets you pause a workflow to wait for human approval. For example, an agent could `search_sciencedirect` for papers, use `get_hosting_permissions` to flag any with publishing restrictions, and then present a list to a team member for review. Only after that person gives the okay does the workflow resume, proceeding to download the full text with `get_article`. This combination of automated search from an MCP Server and manual curation is how you build high-quality, compliant datasets without giving up automation.

Setup guide

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

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

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

Design a workflow where a failed `get_article` call triggers a new step. That step can call `get_article_entitlement` to find out why, allowing your Mastra AI agent to log the specific error and continue.

Yes. A scheduled Mastra AI agent can run `search_sciencedirect` with your topics, fetch abstracts via `get_article`, and assemble a report. The framework's built-in reliability is perfect for this kind of recurring job.

Yes, you can configure the agent to only access a subset of tools from the MCP Server. For example, you could create a 'search-only' agent that can only use `search_sciencedirect` and `get_article_metadata`.

Your agent should use the `get_serial_title` tool. Provide the ISSN, and it returns structured metadata about that specific journal, which is great for verifying sources within a workflow.

The server is a pass-through. It uses article identifiers like DOIs to fetch content from the Elsevier API on your agent's behalf. It does not log or store any of the article data or your queries.

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