How to Use the Marqo AI (Vector Search & Embeddings) MCP in Mastra AI
Build reliable, self-healing search workflows with Mastra AI and Marqo AI vector tools.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Marqo AI (Vector Search & Embeddings) MCP to Mastra AI
Create your Vinkius account to connect Marqo AI (Vector Search & Embeddings) 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.
Automated vector index verification
The `list_indexes` tool provides your workflow with a complete list of active search collections. Before your Mastra agent attempts to read or write, it checks this list to verify the target exists. This prevents empty-state errors in automated pipelines. If the index is missing, your workflow can branch to create it instantly. This programmatic safety net keeps your data pipelines running without human intervention.
Self-healing document indexing pipelines
Writing data with `add_documents` allows your Mastra agent to ingest complex JSON payloads. If an ingestion step fails due to a network hiccup, Mastra's built-in retry engine handles the backoff. Your data gets indexed eventually, even under heavy load. You can configure the workflow to run `get_index_stats` right after ingestion. This verifies the document count increased as expected before moving to the next pipeline step. It ensures high data integrity across your search cluster.
Conditional search routing via Mastra AI MCP Server
Executing `tensor_search` allows your agent to find conceptually similar records in your index. This MCP Server handles the heavy lifting of raw vector math. Mastra can inspect the similarity scores returned by the tool and route the workflow based on confidence thresholds. This conditional logic makes your search agents much more reliable. Instead of showing poor matches, the agent can proactively refine the query or alert a human moderator.
Set up Marqo AI (Vector Search & Embeddings) MCP in Mastra AI
Prerequisites
- Node.js 18+ and a TypeScript project
-
@mastra/mcp+@mastra/corepackages - Active Vinkius subscription with a valid endpoint token
- 1
Install dependencies
Run
npm install @mastra/mcp @mastra/coreplus your preferred model provider (e.g.@ai-sdk/openai). - 2
Configure the MCPClient
Create an
MCPClientwith your Vinkius endpoint as aURLobject. 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'stoolsobject. All Marqo AI (Vector Search & Embeddings) tools become native Mastra tools. - 4
Run with any model
Swap
openai("gpt-4o")for any AI SDK-compatible provider. Callagent.generate()and the agent routes tool calls through MCP automatically.
import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";
const mcpClient = new MCPClient({
id: "marqo-ai-vector-search-embeddings-mcp-client",
servers: {
"marqo-ai-vector-search-embeddings-mcp": {
url: new URL(
"https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
),
},
},
});
const agent = new Agent({
name: "Marqo AI (Vector Search & Embeddings) Agent",
model: openai("gpt-4o"),
instructions: "You have access to Marqo AI (Vector Search & Embeddings) tools.",
tools: {
...(await mcpClient.listTools()),
},
});
const result = await agent.generate(
"List recent Marqo AI (Vector Search & Embeddings) transactions"
);
console.log(result.text);Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Marqo AI. 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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60%
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place for every integration
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Common questions about Marqo AI (Vector Search & Embeddings) MCP in Mastra AI
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