How to Use the Verba MCP in Mastra AI

Q: How does Mastra AI use Verba for knowledge retrieval?

Mastra AI calls performragquery to search the documents. It pulls summarized answers and citations from the Verba knowledge base, allowing your workflow to proceed with verified facts.

Q: Can Mastra AI update data in Verba?

Yes. You can use addknowledgedocument to ingest new content or metadata into the Verba system. This lets you keep complex workflows updated with the latest information.

Q: How do I list all available documents in Verba with Mastra AI?

Call listknowledgedocuments. It pulls an array listing every document indexed in the Verba knowledge base, giving your workflow visibility into the entire dataset.

Build complex workflows connecting Verba knowledge using Mastra AI.

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Connect Verba MCP to Mastra AI

Create your Vinkius account to connect Verba 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 Verba with Mastra AI

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Run advanced RAG searches

The `perform_rag_query` tool executes deep queries against the Verba knowledge base. Your workflow can now retrieve summarized answers and cite exact sources, making the AI's output traceable. This allows your Mastra AI agent to connect with external data sources—the documents—and pass verified information back into a multi-step process.

Manage knowledge records

`add_knowledge_document` ingests new content and metadata directly into the Verba system. You can set up workflows that automatically accept and store incoming documents, keeping your data fresh. If a document is outdated or wrong, you can use `delete_knowledge_document`. Remember, this action permanently removes the record from the knowledge base.

Get system status

`get_system_config` retrieves Verba's current operational configuration. This is key for your Mastra AI agent to make decisions based on the environment it’s running in. It also lets you verify document existence using `get_document_details`, confirming that specific content can be accessed before proceeding with a paid action.

Setup guide

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

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

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

Mastra AI calls `perform_rag_query` to search the documents. It pulls summarized answers and citations from the Verba knowledge base, allowing your workflow to proceed with verified facts.

Yes. You can use `add_knowledge_document` to ingest new content or metadata into the Verba system. This lets you keep complex workflows updated with the latest information.

The server manages document content and associated metadata. Your agent uses these types of structured data when checking system configuration or running a query against Verba.

Call `list_knowledge_documents`. It pulls an array listing every document indexed in the Verba knowledge base, giving your workflow visibility into the entire dataset.

The server deals primarily with structured text: document content and metadata. This means every step—from reading to writing—is built around managing these specific data types.

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