How to Use the N-Gram Frequency Engine MCP in Mastra AI

Build reliable text analysis workflows in Mastra AI. Use the N-Gram Frequency Engine for exact counts in your agent pipelines.

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Connect N-Gram Frequency Engine MCP to Mastra AI

Create your Vinkius account to connect N-Gram Frequency Engine 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 N-Gram Frequency Engine with Mastra AI

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Automated frequency analysis in Mastra AI

Incorporate `extract_ngram_frequencies` into your agent workflows to handle text analysis automatically. If a large document arrives, the agent can trigger this server to get the exact unigram or trigram breakdown. This is perfect for conditional logic where the agent decides what to do next based on specific phrase frequency thresholds. You can set it to retry if the input format is invalid.

Complex text parsing with deterministic results

Use the N-Gram Frequency Engine to parse multi-gigabyte files without bloat. By streaming the text through the tool, your Mastra AI workflow stays under memory limits while producing exact counts. It handles the heavy lifting of punctuation stripping and case normalization before counting. Your downstream agent then receives clean, reliable data to inform its decisions.

Handle failures with built-in retries

Since this is an MCP server, your Mastra AI agent treats it like a native tool. If the network hiccups during a large frequency extraction, the workflow engine manages the retry automatically. This ensures your linguistic analysis pipeline stays running. You get the benefit of a stable, managed service without needing to worry about the underlying infrastructure.

Setup guide

Set up N-Gram Frequency Engine 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 N-Gram Frequency Engine 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: "n-gram-frequency-engine-mcp-client",
  servers: {
    "n-gram-frequency-engine-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent N-Gram Frequency Engine transactions"
);
console.log(result.text);

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Common questions about N-Gram Frequency Engine MCP in Mastra AI

Add the server URL to your Mastra agent configuration. Once registered, you can call `extract_ngram_frequencies` as a standard tool within your agent step definitions.

Yes, it is designed for streaming text processing. It parses data in chunks to ensure your agent doesn't crash from memory issues during heavy analysis.

It is. You can set up human-in-the-loop requirements for the tool, meaning the agent will pause and wait for you to approve the frequency extraction before proceeding.

It uses a deterministic sliding window approach. By normalizing text and counting exact sequences, it eliminates the approximation errors found in other methods.

Your data is handled within an ephemeral V8 sandbox. The text is processed in memory and wiped as soon as the frequency map is generated, keeping it isolated.

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