How to Use the OneLocal LocalReviews MCP in Mastra AI

Q: How do I install the OneLocal LocalReviews server in Mastra AI?

Run npm install @mastra/mcp@latest and initialize the client using new MCPClient. Call mcpClient.listTools() to retrieve the review tools and spread them directly into your agent's tool array.

Q: Can I require human approval before sending reviews via OneLocal LocalReviews and Mastra AI?

Yes, you can configure the agent with requireToolApproval for the requestreview tool. This forces the system to pause and wait for a manager's confirmation before sending an SMS or email invite to a customer.

Q: Does OneLocal LocalReviews support automatic status checks in Mastra AI?

Yes, your agent can run checkonelocalstatus as its first step. If the connection is down, Mastra AI can catch the error and delay the workflow execution rather than failing mid-run.

Q: How do I inspect a single customer review?

Use the getreview tool by passing the specific review ID to your agent. This lets your workflow extract the customer's text and star rating to determine if immediate follow-up is necessary.

Run automated review collection workflows and reputation monitoring agents with Mastra AI.

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Connect OneLocal LocalReviews MCP to Mastra AI

Create your Vinkius account to connect OneLocal LocalReviews to Mastra AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup OneLocal LocalReviews with Mastra AI

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Build automated review request workflows

The `request_review` tool lets your agent send out email or SMS feedback requests based on custom triggers. Mastra AI handles this by executing multi-step workflows, meaning if an invite fails, the engine retries the action automatically with exponential backoff. To track the performance of these automated invites, use `list_campaigns` and `get_campaign` to inspect active marketing pushes. The workflow engine ensures that your outreach campaigns run on schedule without manual intervention or script management.

Run conditional reputation checks with this MCP Server

The `get_reputation` tool provides a quick look at your average ratings and customer sentiment. By integrating this MCP Server into Mastra AI, you can write conditional branches that alert your team on Slack if your rating drops below a specific threshold. You can also use `list_reviews` to scan incoming feedback and isolate negative ratings. If a bad review appears, the agent can automatically trigger internal escalation steps to resolve the customer's issue before it affects your local search ranking.

Manage multi-location business data programmatically

The `list_locations` tool retrieves all active business entities registered on your account through this MCP connection. Mastra AI loops through these locations and uses `get_location` to verify that your business details match what is published online. You can also fetch referral metrics using `list_referrals` to track word-of-mouth growth across different branches. This gives your agent the context it needs to manage large-scale local business operations without manual spreadsheet updates.

Setup guide

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

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

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

Run `npm install @mastra/mcp@latest` and initialize the client using `new MCPClient`. Call `mcpClient.listTools()` to retrieve the review tools and spread them directly into your agent's tool array.

Yes, you can configure the agent with `requireToolApproval` for the `request_review` tool. This forces the system to pause and wait for a manager's confirmation before sending an SMS or email invite to a customer.

Yes, your agent can run `check_onelocal_status` as its first step. If the connection is down, Mastra AI can catch the error and delay the workflow execution rather than failing mid-run.

Use the `get_review` tool by passing the specific review ID to your agent. This lets your workflow extract the customer's text and star rating to determine if immediate follow-up is necessary.

All location profiles, campaign metrics, and referral lists remain private. The MCP Server runs inside an isolated, ephemeral sandbox on Vinkius, meaning no customer details or business configurations are preserved on disk after the tools finish executing.

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