How to Use the Cohere (AI Platform) MCP in Vercel AI SDK

Q: How do I stream Cohere (AI Platform) chat responses using the Vercel AI SDK?

Use the chatgeneration tool inside your Vercel AI SDK streamText call. The MCP client translates Cohere's token stream into standard protocol chunks that stream directly to your frontend.

Q: Can I use Cohere (AI Platform) embeddings inside Vercel AI SDK edge functions?

Yes. The generateembeddings tool runs on edge-compatible runtimes, letting you generate dense vector shapes directly inside Next.js edge routes.

Q: How does the Cohere (AI Platform) reranking tool integrate with Vercel AI SDK?

You pass the rerankdocuments tool to your Vercel AI SDK agent, which sends raw text chunks to Cohere and returns a structured, ranked array that you can map straight to your UI.

Stream live Cohere text generation and document reranking directly into your Vercel AI SDK frontend without loading spinners.

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Connect Cohere (AI Platform) MCP to Vercel AI SDK

Create your Vinkius account to connect Cohere (AI Platform) 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 Cohere (AI Platform) with Vercel AI SDK

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Live document reranking for instant UI updates

The `rerank_documents` tool lets your Vercel AI SDK app reorder search results on the fly and stream the sorted list straight to Next.js components. Instead of making users wait for a slow backend shuffle, this MCP Server hands the ranked array directly to your UI thread. You feed raw text chunks into the tool and watch the sorted results render in real-time. This setup bypasses traditional API bottlenecks by letting the edge function handle the payload sorting immediately.

Real-time conversational transformations

Using the `chat_generation` and `generate_text` tools, your Vercel AI SDK setup can stream Cohere's responses token-by-token directly to your React or Vue frontend. You don't have to write custom streaming wrappers because the MCP client handles the server-sent events out of the box. This means your end users see characters appearing instantly as the model generates them. If you need to check available models first, the `list_models` tool lets the frontend dynamic-switch endpoints without breaking the active stream.

Instant text classification at the edge with MCP Server

This MCP Server exposes the `classify_inputs` tool to run high-speed text categorization directly inside your Vercel AI SDK edge runtime. By running classifications at the edge, you can instantly flag or route user inputs before rendering the next UI state. To keep token counts under control before sending payloads, you can use `tokenize_text` to check structural boundaries. When the session ends, calling `mcpClient.close()` ensures your edge connections clean up immediately without leaking memory.

Setup guide

Set up Cohere (AI Platform) 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 Cohere (AI Platform) 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 Cohere (AI Platform) transactions",
});

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cohere. 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 Cohere (AI Platform) MCP in Vercel AI SDK

Use the `chat_generation` tool inside your Vercel AI SDK streamText call. The MCP client translates Cohere's token stream into standard protocol chunks that stream directly to your frontend.

Yes. The `generate_embeddings` tool runs on edge-compatible runtimes, letting you generate dense vector shapes directly inside Next.js edge routes.

You pass the `rerank_documents` tool to your Vercel AI SDK agent, which sends raw text chunks to Cohere and returns a structured, ranked array that you can map straight to your UI.

Configure an authProvider when calling createMCPClient to handle your Vinkius token. This manages secure access to the Cohere endpoint without hardcoding keys in your frontend code.

Your text inputs and document chunks are processed in an isolated V8 sandbox on Vinkius. No data is stored on our servers, and payloads go directly to Cohere via encrypted transit.

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