How to Use the Eden AI MCP in Vercel AI SDK

Q: How do I connect Eden AI to Vercel AI SDK?

Install @ai-sdk/mcp. Call createMCPClient with your HTTP transport URL, then pass mcpClient.tools() into generateText or streamText. Always remember to call mcpClient.close() when the session ends.

Q: Can Vercel AI SDK stream Eden AI workflow configurations?

Yes. When the agent calls getworkflowconfiguration, the SDK streams the JSON response back to your React frontend in chunks. The UI updates as the payload arrives.

Q: How do I filter which models Eden AI exposes to the client?

You pull the full list using listallllmmodels. Then, handle the filtering logic within your Vercel AI SDK prompt instructions before displaying the options to the user.

Q: What data privacy rules apply to Eden AI usage statistics?

The getapiusagestatistics tool reads your aggregate API consumption and cost metrics. Vinkius executes this request within an ephemeral V8 Isolate sandbox. The session drops immediately after the response, leaving zero persistent traces of your financial metadata.

Give your Vercel AI SDK frontend direct access to Eden AI provider pricing and workflow stats, streaming results right to the UI.

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Connect Eden AI MCP to Vercel AI SDK

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

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Stream Eden AI Pricing to Vercel AI SDK

The `get_ai_feature_pricing` and `list_ai_providers` tools let your Next.js application pull live cost matrices from different vendors. When a user asks about the cheapest text-to-speech option, the SDK calls the MCP Server and streams the exact rates back to the client. You don't wait for a loading spinner. You also get `quick_ai_provider_audit` to run health checks on text analysis endpoints. The agent pulls the summary and pipes it into your React components instantly. It gives your users real-time visibility into which models are currently available.

Expose Unified Workflows

With `list_ai_workflows` and `get_workflow_configuration`, your edge functions read the exact steps configured in your Eden AI account. The AI client checks the pipeline settings before executing a request. This prevents blind calls to misconfigured endpoints. If you need to know what subfeatures exist, `list_available_ai_features` maps out the supported capabilities. You pass these details straight to the user interface. They see exactly what operations the platform supports right now.

Track Account Metrics via MCP

The `get_api_usage_statistics` tool pulls raw technical data on API consumption and costs. Your Vercel AI SDK agent grabs these numbers and renders a live dashboard in your Svelte or Vue app. It keeps the finance team updated without manual exports. Add `get_eden_ai_metadata` to check your current credit balance. If credits run low, the AI triggers a warning alert in the UI before a major batch job starts. You stop failures before they happen.

Setup guide

Set up Eden AI 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 Eden AI 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 Eden AI 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 Eden AI. 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 Eden AI MCP in Vercel AI SDK

Install `@ai-sdk/mcp`. Call `createMCPClient` with your HTTP transport URL, then pass `mcpClient.tools()` into `generateText` or `streamText`. Always remember to call `mcpClient.close()` when the session ends.

Yes. When the agent calls `get_workflow_configuration`, the SDK streams the JSON response back to your React frontend in chunks. The UI updates as the payload arrives.

Yes. You run the MCP client inside your edge runtime. It handles the HTTP transport to the Vinkius-hosted server without requiring Node.js core modules.

You pull the full list using `list_all_llm_models`. Then, handle the filtering logic within your Vercel AI SDK prompt instructions before displaying the options to the user.

The `get_api_usage_statistics` tool reads your aggregate API consumption and cost metrics. Vinkius executes this request within an ephemeral V8 Isolate sandbox. The session drops immediately after the response, leaving zero persistent traces of your financial metadata.

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