How to Use the PandaDoc MCP in Vercel AI SDK

Q: What is the setup process for using the PandaDoc MCP Server inside Vercel AI SDK?

First, install the @ai-sdk/mcp package and instantiate the PandaDoc client. Then, pass the tools array directly into streamText to let your Vercel AI SDK agent call getdocumentdetails or other endpoints.

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Connect PandaDoc MCP to Vercel AI SDK

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Setup PandaDoc with Vercel AI SDK

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Build live contract generators with this MCP Server

`create_document` lets your application generate customized contracts instantly while streaming the UI states to your React or Next.js app. Your Vercel AI SDK client pulls the PandaDoc template schema, maps the dynamic fields, and triggers the file creation without making the user wait for a page reload. After the draft is ready, the server uses `send_document` to dispatch the agreement straight to the client's inbox while the Vercel AI SDK updates the chat UI. This PandaDoc workflow runs entirely inside Vercel Edge Functions, keeping your API overhead low and your React frontend highly responsive.

Embed signature sessions directly in your web app

`create_signing_session` generates a direct signing link that you can pipe straight into your Vercel AI SDK chat interface. Instead of redirecting users to an external portal, your Next.js application displays the PandaDoc signing frame immediately. By monitoring the document state with `get_document_details`, your React frontend updates the interface as soon as the client signs. This eliminates the need for manual polling or complex webhook setups on your Vercel AI SDK backend.

Search templates and documents on the fly

`list_templates` fetches your active contract layouts directly into your Vercel AI SDK streaming context using this MCP Server. Your React agent parses the PandaDoc template options and presents them to your user as a clean, interactive list. If you need to find past agreements, `list_documents` lets you query files by status like draft or sent within your Next.js app. The PandaDoc results stream straight into your Vercel AI SDK UI component, updating the screen as the data arrives.

Setup guide

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

The Vercel AI SDK handles it by streaming state updates. While `create_document` processes the PandaDoc template, the Vercel AI SDK streams partial UI blocks to your users, keeping them engaged while the API works.

Yes, this MCP Server is lightweight enough to execute within Vercel AI SDK Edge runtimes. You call `list_templates` to retrieve PandaDoc layouts, and the SDK processes the payload directly on the edge.

You can intercept the tool call in your Next.js code before running `send_document`. The Vercel AI SDK lets you render a confirmation UI, letting the user inspect the PandaDoc metadata before the agent executes the final dispatch.

First, install the `@ai-sdk/mcp` package and instantiate the PandaDoc client. Then, pass the tools array directly into `streamText` to let your Vercel AI SDK agent call `get_document_details` or other endpoints.

This MCP integration operates within a secure sandbox that never exposes your raw PandaDoc API keys to the browser. Your template UUIDs and recipient emails are processed on the server-side, ensuring that sensitive PandaDoc document metadata remains isolated from the Vercel AI SDK frontend client.

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