How to Use the Mashvisor MCP in Mastra AI

Q: How do I configure Mastra AI to use Mashvisor?

Install @mastra/mcp and instantiate a new MCPClient object. Map the Vinkius URL to your server configuration. Spread the tools into your agent definition to enable real estate queries.

Q: Can Mastra AI retry failed Mashvisor tool calls?

Yes. If the getairbnblistings tool times out, Mastra automatically executes its exponential backoff protocol. Your workflow recovers and completes the property search without breaking the pipeline.

Q: How do I require human approval for Mashvisor queries in Mastra AI?

You set the requireToolApproval flag on your agent configuration. When the workflow attempts to run getinvestmentanalysis, execution pauses until an administrator approves the request.

Q: What happens to my investment criteria when using this MCP server?

The target neighborhoods and price filters you send to listneighborhoods run inside an ephemeral zero-trust container. Vinkius tears down the environment immediately after returning the market data. We never log your investment strategies.

Automate complex real estate analysis workflows with Mashvisor and Mastra AI.

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Connect Mashvisor MCP to Mastra AI

Create your Vinkius account to connect Mashvisor 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 Mashvisor with Mastra AI

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Build Mastra AI Investment Pipelines

The `search_properties` tool acts as the trigger for your Mastra AI automated real estate pipelines. Your workflow queries specific cities, and if the initial search fails due to network lag, Mastra triggers an automatic retry with exponential backoff. The agent pulls active properties with investment metrics without manual oversight. You pass the Mashvisor MCP server URL into the `MCPClient` configuration. Calling `mcpClient.listTools()` exposes the endpoints to your agent, letting it filter the results by bedrooms and property type using `list_properties`.

Branch Logic on Rental Performance

The `get_investment_analysis` tool returns calculated capitalization rates that your workflow engine uses for conditional branching. If the cash-on-cash return for an Airbnb strategy exceeds your threshold, the agent routes the data to one notification channel. If traditional rentals perform better, it routes elsewhere. To verify the short-term strategy, the agent queries `get_historical_performance` to check seasonality. You build resilient, multi-step financial evaluations that handle bad data gracefully before moving to the next pipeline stage.

Cross-Reference Property Metrics

The `get_property` tool fetches granular property attributes that your agent cross-references against local market data. The workflow pulls the specific building details, then automatically queries `get_rental_rates` to evaluate the traditional rental potential. Mastra executes this multi-tool sequence natively. You can add human-in-the-loop validation using `requireToolApproval` before the agent finalizes the report. The MCP Server executes the heavy lifting by fetching rent distribution statistics while your code manages the logic.

Setup guide

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

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

const result = await agent.generate(
  "List recent Mashvisor transactions"
);
console.log(result.text);

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Common questions about Mashvisor MCP in Mastra AI

Install `@mastra/mcp` and instantiate a new `MCPClient` object. Map the Vinkius URL to your server configuration. Spread the tools into your agent definition to enable real estate queries.

Yes. If the `get_airbnb_listings` tool times out, Mastra automatically executes its exponential backoff protocol. Your workflow recovers and completes the property search without breaking the pipeline.

You set the `requireToolApproval` flag on your agent configuration. When the workflow attempts to run `get_investment_analysis`, execution pauses until an administrator approves the request.

Mastra automatically negotiates whether to use Streamable HTTP or SSE based on the endpoint. You just provide the Vinkius URL and the framework connects to the endpoints.

The target neighborhoods and price filters you send to `list_neighborhoods` run inside an ephemeral zero-trust container. Vinkius tears down the environment immediately after returning the market data. We never log your investment strategies.

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