Cacheflow MCP Server for Mastra AIGive Mastra AI instant access to 6 tools to Create Proposal, Get Approval Requests, Get Proposal Details, and more
Mastra AI is a TypeScript-native agent framework built for modern web stacks. Connect Cacheflow through Vinkius and Mastra agents discover all tools automatically. type-safe, streaming-ready, and deployable anywhere Node.js runs.
Ask AI about this App Connector for Mastra AI
The Cacheflow app connector for Mastra AI 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 { Agent } from "@mastra/core/agent";
import { createMCPClient } from "@mastra/mcp";
import { openai } from "@ai-sdk/openai";
async function main() {
// Your Vinkius token. get it at cloud.vinkius.com
const mcpClient = await createMCPClient({
servers: {
"cacheflow": {
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
},
});
const tools = await mcpClient.getTools();
const agent = new Agent({
name: "Cacheflow Agent",
instructions:
"You help users interact with Cacheflow " +
"using 6 tools.",
model: openai("gpt-4o"),
tools,
});
const result = await agent.generate(
"What can I do with Cacheflow?"
);
console.log(result.text);
}
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.
Mastra's agent abstraction provides a clean separation between LLM logic and Cacheflow tool infrastructure. Connect 6 tools through Vinkius and use Mastra's built-in workflow engine to chain tool calls with conditional logic, retries, and parallel execution. deployable to any Node.js host in one command.
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 Mastra AI 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 Mastra AI
When Mastra AI 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 Mastra AI via MCP
Follow these steps to wire Cacheflow into Mastra AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
npm install @mastra/core @mastra/mcp @ai-sdk/openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.ts and run with npx tsx agent.tsExplore tools
Why Use Mastra AI with the Cacheflow MCP Server
Mastra AI provides unique advantages when paired with Cacheflow through the Model Context Protocol.
Mastra's agent abstraction provides a clean separation between LLM logic and tool infrastructure. add Cacheflow without touching business code
Built-in workflow engine chains MCP tool calls with conditional logic, retries, and parallel execution for complex automation
TypeScript-native: full type inference for every Cacheflow tool response with IDE autocomplete and compile-time checks
One-command deployment to any Node.js host. Vercel, Railway, Fly.io, or your own infrastructure
Cacheflow + Mastra AI Use Cases
Practical scenarios where Mastra AI combined with the Cacheflow MCP Server delivers measurable value.
Automated workflows: build multi-step agents that query Cacheflow, process results, and trigger downstream actions in a typed pipeline
SaaS integrations: embed Cacheflow as a first-class tool in your product's AI features with Mastra's clean agent API
Background jobs: schedule Mastra agents to query Cacheflow on a cron and store results in your database automatically
Multi-agent systems: create specialist agents that collaborate using Cacheflow tools alongside other MCP servers
Example Prompts for Cacheflow in Mastra AI
Ready-to-use prompts you can give your Mastra AI 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 Mastra AI
Common issues when connecting Cacheflow to Mastra AI through the Vinkius, and how to resolve them.
createMCPClient not exported
npm install @mastra/mcpCacheflow + Mastra AI FAQ
Common questions about integrating Cacheflow MCP Server with Mastra AI.
How does Mastra AI connect to MCP servers?
MCPClient with the server URL and pass it to your agent. Mastra discovers all tools and makes them available with full TypeScript types.