This isn’t a product launch announcement. We just want to share why we built this and how it might be useful to you.
A Recurring Problem
Over the past year, our team has been building AI Agent projects. And every time we start something new, we run into the same issue:
Integrating MCP Servers.
MCP itself is a great protocol. It allows AI systems to call external tools — which is essential if you want Agents to actually do things instead of just generating text.
But the integration process is almost always the same:
- Find the server’s repository
- Read the documentation and figure out the configuration
- Write config files
- Set up environment variables
- Possibly spin up a Docker container
- Deal with permission issues
- Hope everything works
Integrating one server is manageable.
But when your Agent needs GitHub, Slack, Calendar, Email, and a database — all at once — the process quickly becomes painful.
Our Initial Solution
At first, we wrote internal scripts to simplify this workflow.
Over time, those scripts evolved into a small SDK.
The core question we kept asking ourselves was:
Can we reduce “integrating an MCP Server” to a single line of code?
Ideally, something like this:
const github = await mcpsdk.connect('github');
No config files.
No Docker management.
No permission juggling.
Just specify the server you want — and let the SDK handle the rest.
How It Works
Under the hood, MCPSDK does a few key things:
1. Unified Registry
We aggregate MCP Servers from multiple sources — official, community, and third-party.
Currently, there are 5000+ available servers accessible through a unified interface.
2. Cloud Sandbox Execution
Servers don’t run locally.
They run inside secure sandboxes built on Sandock.ai.
This eliminates:
- Local environment inconsistencies
- Permission conflicts
- Manual process management
- Security isolation headaches
3. Standardized Interface
No matter:
- What language the underlying server is written in
- How it’s configured internally
- Where it’s hosted
The interface you call remains consistent.
From the developer’s perspective, everything feels uniform.
This Isn’t a Silver Bullet
We want to be honest about trade-offs.
This approach has limitations:
- Latency — Cloud execution introduces network round trips. Not ideal for latency-sensitive workloads.
- Cost — Sandbox execution isn’t free. High-frequency workloads must account for this.
- Customization — Deep customization may still be easier with local deployment.
We’re not trying to replace local setups.
Instead, we’re offering a “quick start” path.
When you’re prototyping or validating an idea, you shouldn’t have to spend half a day configuring infrastructure.
Who Might Use This
From our experience, MCPSDK may be useful for:
- Teams building AI Agents that need fast multi-service integration
- Workflow automation products connecting multiple APIs
- Developers experimenting with MCP who want to avoid configuration overhead
Want to Try It?
If this sounds relevant to what you’re building:
Visit https://mcpsdk.dev
Documentation: https://mcpsdk.dev/docs
You can also open an issue on GitHub or reach out via Discord.
We’re still iterating. Many features aren’t fully polished yet.
But if you're willing to try it — we’d genuinely love your feedback.
If you'd like, I can also produce:
- A slightly more narrative / story-driven Medium version
- A more technical version for Hacker News
- A more opinionated version for X / LinkedIn
- Or a sharper, punchier version optimized for engagement
Just tell me which direction you want.
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