How to Use the Mapflow MCP in Vercel AI SDK

Stream geospatial insights directly into your Vercel AI SDK interface without waiting for background jobs to finish.

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Connect Mapflow MCP to Vercel AI SDK

Create your Vinkius account to connect Mapflow to Vercel AI SDK 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 Mapflow with Vercel AI SDK

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Real-time satellite feature extraction

Your AI client triggers `create_processing` to identify building footprints or road networks from raw imagery. The results stream straight into your frontend components as soon as the data hits the wire. This bypasses the usual lag of polling servers. Your users get immediate visual feedback on their maps while the processing pipeline runs in the background.

Dynamic model selection for GIS

Use `list_models` to fetch available detection algorithms based on your specific terrain requirements. You can switch between vegetation or urban density models before starting a task. It keeps your interface clean. You only show the user the settings that actually apply to the current satellite tile.

Manage imagery projects in the browser

Call `list_projects` to render a live dashboard of ongoing geospatial jobs within your React or Next.js app. Users can organize their analysis buckets without leaving your UI. Everything stays in sync. When an agent creates a new workspace, your components update instantly to reflect the new state.

Setup guide

Set up Mapflow MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all Mapflow tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent Mapflow transactions",
});

console.log(text);
await mcpClient.close();

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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 Mapflow. 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 Mapflow MCP in Vercel AI SDK

Install the MCP packages and pass the server tools into your streamText call. This allows your agent to execute Mapflow functions as part of its normal reasoning loop.

Yes. Use the MCP client to initiate the job and store the ID, then have your app poll the status asynchronously. This keeps your serverless functions from hitting execution timeouts.

The MCP protocol provides the bridge for tool calls, but you should handle the final data ingestion in your client-side state. Use standard React hooks to display the polygons once the job completes.

Mapflow stores your uploaded satellite tiles in a secure environment during the analysis phase. Once the job is finished, you access the results through the API and handle the persistence in your own database.

Your endpoint token is handled by the Vinkius infrastructure. The server isolates your credentials so they never leak into your client-side code.

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