How to Use the Docamatic MCP in Vercel AI SDK

Q: Does Docamatic support template management in Vercel AI SDK?

It does. Use listavailablebuiltintemplates to fetch your options. You can then map your local state variables to the template fields.

Use the Docamatic MCP Server with Vercel AI SDK to render PDFs directly in your React or Next.js app flows.

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Connect Docamatic MCP to Vercel AI SDK

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

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Real-time PDF generation for Vercel AI SDK

Trigger `generate_pdf_from_html` directly from your edge functions. Your AI client receives the rendered output instantly, bypassing the need for heavy backend worker queues. Data binding happens on the fly. Pass your JSON objects into `generate_pdf_from_template` and watch the document populate as the stream completes.

Dynamic document assembly

Combine multiple generated files into one report using `merge_multiple_pdfs`. Your AI client handles the array of file paths and returns a single, production-ready document. Keep track of what you built. Use `list_generation_history` to pull logs into your dashboard for immediate user verification.

Visual capture within your interface

Implement `take_web_screenshot` to grab full-page site captures. Your AI client can now process visual context alongside your textual data. Convert any URL to a static image with `generate_image_from_html`. It's a quick way to generate thumbnails for your document history without manual effort.

Setup guide

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

Install the MCP package and point your client transport to the server URL. You'll then pull the tool definitions into your generateText call to start rendering.

Yes. Since the server runs independently, your Vercel functions remain lean. You only handle the final byte transfer once the PDF is ready.

It does. Use `list_available_builtin_templates` to fetch your options. You can then map your local state variables to the template fields.

Docamatic returns the file path or buffer directly to your agent. You decide where to save or serve the final output.

Your data is processed ephemerally within the V8 sandbox. Once the PDF generation finishes, the memory is cleared, ensuring no sensitive content lingers.

Use it with your favorite AI tools

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