Let me be real with you.

I still remember when we first tried integrating multiple AI tools for a client project last year. It felt like herding cats. One tool needed an API bridge. Another needed a custom wrapper. And then... boom, the third tool crashed everything because it couldn’t “talk” to the others.

Honestly, I almost rage-quit that setup.

But then we discovered MCP — Model Context Protocol — and it genuinely changed how we build AI systems at Destinova AI Labs.

---

Why We’re So Into MCP (And You Might Be Too):

• You don’t need to code custom connections anymore. Seriously. Zero wrappers.
• You can pull from APIs, databases, and files without losing context.
• Your AI can now do things like search GitHub, manage files, or even execute Docker containers.
• It’s like giving your AI a superpower... without babysitting it.

Ever wondered what it’d be like if your AI could auto-triage GitHub issues or scan for API response structures just because you asked?

Let’s walk through some of the MCP servers we’ve been testing — and why we’re borderline addicted to them.

---

1. GitHub MCP Server – Our Codebase Therapist

We use this daily. No joke. Our AI can now create branches, find vulnerabilities, and push commits without us lifting a finger. The setup is a bit Docker-y, but once it’s live, it’s like having a junior dev who never forgets linting.
What It Does: Turns your AI into a repository expert, automating tasks like branch management and issue triage.

Setup:

Install Docker and get a GitHub Personal Access Token.
Clone the repo: git clone https://github.com/github/github-mcp-server.git
Set GITHUB_PERSONAL_ACCESS_TOKEN.
Run:

bash
docker run -i --rm -e GITHUB_PERSONAL_ACCESS_TOKEN=${env:GITHUB_TOKEN} ghcr.io/github/github-mcp-server

Features:
Repo Management: Create/fork repos, manage branches, search code.
Code Operations: Edit files, commit, push updates.
Collaboration: Handle issues, PRs, comments.

Use **Cases:
Auto-triaging issues.
Creating boilerplates in seconds.
Scanning for vulnerabilities.
**Pro Tip: Use connection pooling and secure token storage.

---

2. Apidog MCP Server – For When You’re Too Tired to Read Docs

You know those days when your brain just can’t handle scanning OpenAPI specs? Yeah. Now we just ask our AI, “What’s the payload for /users?” — and bam, instant answer. It’s magic.

What It Does: Connects AI to your API docs for easy endpoint queries.

Setup:
In Cursor: Settings > MCP > Add Server
Paste config, embed update <access-token> and <project-id>.

Features:
Syncs with Apidog or local OpenAPI files.
Natural language queries like: “What’s the response for /users?”
Offline spec caching.

Use Cases:
Generate TypeScript interfaces.
Build Python clients quickly.
Debug APIs easily.
Pro Tip: Update specs regularly.

---

3. Brave Search MCP Server – Because Privacy Matters

We’ve always hated that feeling of being tracked while researching. Brave Search MCP gives you clean, AI-queriable results — and zero tracking. It’s like DuckDuckGo but with context-awareness and less shouting into the void.

What It Does: Offers web search with privacy focus—ideal for documentation.

Setup:
Get a Brave Search API key (2,000 free queries/month).
Use with stdio or SSE transport.

Features:
Filter by type, safety, freshness.
Location-based + web fallback.
Independent index = better privacy.

Use Cases:
Find coding tutorials.
Research privately.
Merge local + web docs.
Pro Tip: Tune pagination settings.

READ MORE to get our full setup guide, tips from our devs, and honest feedback on what worked — and what didn’t.