How to Use the Ensembl MCP in Mastra AI

Q: Is my proprietary genomic sequence data safe with Mastra AI?

Yes, because all sequence data fetched via getsequenceid is processed in memory on your host server. The MCP integration acts as a zero-trust proxy, meaning your gene sequences are never written to disk or logged.

Build reliable genomic workflows with Mastra AI and direct Ensembl database access.

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Connect Ensembl MCP to Mastra AI

Create your Vinkius account to connect Ensembl 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 Ensembl with Mastra AI

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Claude

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Build fault-tolerant variant pipelines with Mastra AI

`get_vep_id` retrieves variant consequence predictions within your automated Mastra AI workflows. If Ensembl's server rate-limits your request, Mastra's built-in retry engine automatically backs off and tries the query again. You can route failing IDs to `get_vep_bulk` as a fallback batch. This ensures your genomic annotation pipeline never crashes mid-run due to transient network hiccups or API spikes.

Route genomic analyses based on species metadata

`get_taxonomy_id` fetches taxonomic classifications to branch your Mastra AI agent workflows. Your workflow can check the taxonomy of a sample and dynamically decide which downstream analysis path to execute. Once the species is confirmed, the agent calls `get_info_assembly` to load the correct reference genome version. This prevents your pipeline from mapping human variants to mouse assemblies by mistake.

Automate gene tree analysis using this MCP Server

`get_genetree` retrieves comparative genomic trees for any stable gene identifier within Mastra AI. Your agent can parse the tree structure to find duplication events or evolutionary divergence points automatically. The workflow then triggers `get_homology` to identify orthologs across target species. Because Mastra supports conditional branching, you can set up human-in-the-loop approvals before running deep alignments on massive gene families.

Setup guide

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

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

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

Instantiate the client to connect this MCP Server with your Vinkius endpoint URL and pass the tools to your agent. Your workflow can then call tools like `get_lookup_id` or `get_sequence_id` natively during execution steps.

Yes, you can design workflows that split large gene lists and run `get_lookup_bulk` in parallel. Mastra manages the state of each branch and aggregates the genomic metadata automatically.

Define an exponential backoff policy in your Mastra workflow configuration block. When tools like `get_alignment` time out, the framework retries the operation without failing the entire genomic analysis run.

Yes, Mastra AI allows you to set up approval gates for specific tool calls. You can pause the workflow before running a heavy `get_overlap_region` query to avoid unnecessary API consumption.

Yes, because all sequence data fetched via `get_sequence_id` is processed in memory on your host server. The MCP integration acts as a zero-trust proxy, meaning your gene sequences are never written to disk or logged.

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