Cover image for I gave my Cursor agent real tools without five API keys

Neha Prasad

I Tried Building an AI Agent That Does Real Work. The Biggest Bottleneck Wasn't the Model.

Last week, I wanted to build an AI agent that could do more than chat.

My goal was simple:

  • Drive a live browser through our product
  • Find a small ICP lead list
  • Enrich work email addresses
  • Draft personalized outreach in our brand tone

Surprisingly, the LLM wasn't the bottleneck.

The real challenge was the setup.

The Setup Problem

Every capability required another tool:

  • Another vendor account
  • Another API key
  • Another subscription
  • Another credit card

For a one-off experiment, I spent more time connecting tools than actually building the workflow.

So I tried using Vaaya as a single MCP server inside Cursor.

Installation

Install Vaaya MCP:

npx @vaaya/mcp install

Enter fullscreen mode Exit fullscreen mode

It automatically added this to my Cursor MCP configuration:

{
  "mcpServers": {
    "vaaya": {
      "command": "npx",
      "args": ["-y", "@vaaya/mcp"]
    }
  }
}

Enter fullscreen mode Exit fullscreen mode

Then I simply:

  1. Restarted Cursor
  2. Opened Settings → MCP and verified that vaaya was running
  3. Switched to Agent Chat (Tools Mode)
  4. Approved the one-time connect/signup prompt on first use

That was the entire onboarding experience.

The Prompt I Used 

Using Vaaya tools:

1. Open our product in a live browser and record a short walkthrough.
2. Find 15 Series A SaaS Product Managers matching our ICP.
3. Enrich their work email addresses.
4. Draft short outreach in our brand tone.

Show every step while you work.

Do not send anything.
Drafts only.

Enter fullscreen mode Exit fullscreen mode

I intentionally kept everything in draft mode so I could review the output before anything was sent.

What Worked Well

✅ One MCP instead of managing multiple vendor accounts for a demo

✅ Tool calls were visible inside the agent UI, making it easier to understand and debug what the agent was doing

✅ Pay-per-successful-call felt much better for experimentation than paying for multiple monthly subscriptions

What I Wouldn't Skip

  • Always review outputs before sending
  • Start with a single workflow instead of trying to automate your entire GTM process
  • Keep your first experiment small (15 leads instead of 500)

Who This Is For

If you're a:

  • Founder
  • DevRel engineer
  • AI engineer
  • Developer building agent workflows or demos

…and you're spending more time wiring APIs than testing ideas, this approach is worth trying.

It's not magic.

It just removes a lot of the setup friction between:

"I have an idea."

and

"My agent actually called the tools."

You can check it out here:

https://vaaya.ai/
https://www.linkedin.com/company/vaayaio/posts/
https://x.com/getvaaya

and follow this pages!

If you've built similar agent workflows, I'd love to know what your stack looks like.

What part of the setup still feels the most painful?

Tags

#AI #MCP #Cursor #AIAgents #AgenticAI #DeveloperTools #Productivity #Automation #LLM #DevTools