How to Use the Mapillary MCP in Mastra AI

Q: Can Mastra AI automate the detection of Mapillary signs?

Absolutely. You build a workflow that calls getimagedetections for every image in a sequence. The agent then logs the detected signs to your database.

Q: Is it possible to chain Mapillary tools in Mastra AI?

Yes. You can create a sequence where searchimages feeds into getimagedetections. This creates a multi-step audit pipeline.

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Connect Mapillary MCP to Mastra AI

Create your Vinkius account to connect Mapillary 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 Mapillary with Mastra AI

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Define complex workflows that trigger `search_sequences` based on your project area. Your agents can automatically identify gaps in imagery and log them for review. Use `get_sequence` to pull image metadata and verify if the data is recent enough for your audit. If the images are too old, your agent can flag the sequence for a manual check.

Detection pipelines in Mastra AI

Chain `get_image_detections` into your agent logic to count traffic signs or identify road defects automatically. This removes the need for manual image inspection. Pass the results to `get_detection_value` to verify the specific type of object found. Your workflow can automatically trigger an alert if a critical sign is missing.

Spatial data retrieval for Mastra AI

Query `get_map_features` to generate a comprehensive inventory of street objects. You can use these features to build a live map of your district's infrastructure. Your Mastra AI agents can filter these results by confidence score. This ensures your final report only includes high-certainty detections.

Setup guide

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

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

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

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Common questions about Mapillary MCP in Mastra AI

Absolutely. You build a workflow that calls `get_image_detections` for every image in a sequence. The agent then logs the detected signs to your database.

You can implement exponential backoff directly in your agent workflow. This ensures your requests stay within the limits defined by the API.

Yes. You can create a sequence where `search_images` feeds into `get_image_detections`. This creates a multi-step audit pipeline.

Your workflow can catch the error and trigger a retry or notify an admin. You have full control over the failure handling logic.

The connection uses an ephemeral endpoint token. We only process the specific image metadata and detection coordinates required for your audit.

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