Cacheflow MCP Server for Vercel AI SDKGive Vercel AI SDK instant access to 6 tools to Create Proposal, Get Approval Requests, Get Proposal Details, and more
The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect Cacheflow through Vinkius and every tool is available as a typed function. ready for React Server Components, API routes, or any Node.js backend.
Ask AI about this App Connector for Vercel AI SDK
The Cacheflow app connector for Vercel AI SDK is a standout in the Sales Automation category — giving your AI agent 6 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import { createMCPClient } from "@ai-sdk/mcp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
async function main() {
const mcpClient = await createMCPClient({
transport: {
type: "http",
// Your Vinkius token. get it at cloud.vinkius.com
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
});
try {
const tools = await mcpClient.tools();
const { text } = await generateText({
model: openai("gpt-4o"),
tools,
prompt: "Using Cacheflow, list all available capabilities.",
});
console.log(text);
} finally {
await mcpClient.close();
}
}
main();
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Cacheflow MCP Server
Connect your Cacheflow account to any AI agent and take full control of your automated sales proposals and checkout workflows through natural conversation.
The Vercel AI SDK gives every Cacheflow tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 6 tools through Vinkius and stream results progressively to React, Svelte, or Vue components. works on Edge Functions, Cloudflare Workers, and any Node.js runtime.
What you can do
- Proposal Orchestration — List and manage active sales proposals programmatically, including monitoring their status (sent, viewed, signed) and retrieving detailed metadata
- Approval Workflow Intelligence — Access your pending approval requests to maintain a high-velocity sales cycle and oversee the internal signing pipeline in real-time
- CRM Ecosystem Sync — Programmatically trigger the synchronization of proposal data to your connected Salesforce or HubSpot instance to ensure high-fidelity records
- Customer Oversight — Retrieve complete directories of external customers synced from your CRM to maintain a perfectly coordinated relationship ecosystem
- Revenue Visibility — Access specific proposal details and monitor sales performance metrics directly through your agent for instant operational reporting
The Cacheflow MCP Server exposes 6 tools through the Vinkius. Connect it to Vercel AI SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 6 Cacheflow tools available for Vercel AI SDK
When Vercel AI SDK connects to Cacheflow through Vinkius, your AI agent gets direct access to every tool listed below — spanning cpq, sales-proposals, b2b-checkout, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Pass data as a JSON string. Create a new sales proposal
List pending approvals for me
Get specific proposal details
List external customers
List all sales proposals
Sync proposal to CRM
Connect Cacheflow to Vercel AI SDK via MCP
Follow these steps to wire Cacheflow into Vercel AI SDK. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
npm install @ai-sdk/mcp ai @ai-sdk/openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the script
agent.ts and run with npx tsx agent.tsExplore tools
Why Use Vercel AI SDK with the Cacheflow MCP Server
Vercel AI SDK provides unique advantages when paired with Cacheflow through the Model Context Protocol.
TypeScript-first: every MCP tool gets full type inference, IDE autocomplete, and compile-time error checking out of the box
Framework-agnostic core works with Next.js, Nuxt, SvelteKit, or any Node.js runtime. same Cacheflow integration everywhere
Built-in streaming UI primitives let you display Cacheflow tool results progressively in React, Svelte, or Vue components
Edge-compatible: the AI SDK runs on Vercel Edge Functions, Cloudflare Workers, and other edge runtimes for minimal latency
Cacheflow + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the Cacheflow MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query Cacheflow in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate Cacheflow tools and return structured JSON responses to any frontend
Chatbots with tool use: embed Cacheflow capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with Cacheflow through natural language queries
Example Prompts for Cacheflow in Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Cacheflow immediately.
"List all active sales proposals in my account."
"Show my pending internal approval requests."
"Sync proposal 'prop_123' to HubSpot."
Troubleshooting Cacheflow MCP Server with Vercel AI SDK
Common issues when connecting Cacheflow to Vercel AI SDK through the Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcpCacheflow + Vercel AI SDK FAQ
Common questions about integrating Cacheflow MCP Server with Vercel AI SDK.
How does the Vercel AI SDK connect to MCP servers?
createMCPClient from @ai-sdk/mcp and pass the server URL. The SDK discovers all tools and provides typed TypeScript interfaces for each one.Can I use MCP tools in Edge Functions?
Does it support streaming tool results?
useChat and streamText that handle tool calls and display results progressively in the UI.