Intro: The Evolution of AI Interaction

In the early days, AI models could only respond to prompts with generated text—limited to their internal knowledge and training data.
They were like brilliant conversationalists trapped in a box, unable to reach out and interact with the real world.

The Rise of Web Use

Then came web use: AI agents gained the ability to browse the internet, pulling in live data from websites to inform their responses.
This opened up a new era of dynamic, context-aware AI.

The Era of Tool Use

Next evolved different flavors of integration tool use, where agents could connect to APIs, databases, and services—querying CRMs, processing IoT streams, or even automating workflows.
But these integrations were often ad-hoc, leading to brittle setups that broke easily.

Enter the Model Context Protocol (MCP)

MCP is a standardized interface for how AI interacts with the world.
It defines a clear contract for agents to:

• List resources (e.g., files in a drive)
• Read data
• Call tools (e.g., search, convert formats)

In essence, MCP is an API blueprint for AI, promising seamless plug-and-play across systems without endless custom code.

The MCP Explosion

As MCP caught on, everyone and their mom started building them.

• Notion? There's an MCP for that, letting agents query notes and databases.
• Figma? An MCP to manipulate designs and export assets.

This sparked a race to develop an MCP for every tool imaginable—Google Drive, Salesforce, Slack, you name it.
Libraries and protocols popped up overnight, hyped as the ultimate solution for agentic AI.

The Pitfalls of Generic MCPs

This proliferation sounds great in theory, but generic, general-purpose MCPs are hard to deal with in practice.

• They're designed to be one-size-fits-all, covering every possible feature of a tool or data source.
• But do you know every Excel functionality? Pivot tables, macros, conditional formatting—it’s endless.
• Do you know every Figma functionality? Layers, prototypes, plugins—also endless.

Why confuse the AI with access to 2000 tools and sub-functions?
Agents end up guessing how to navigate these sprawling interfaces, leading to:

• Errors: Misreading files, choking on large datasets without proper chunking.
• Critical failures: Bad for mission-critical tasks like financial analysis or compliance reporting.
• Security headaches: Command injection and SSRF vulnerabilities have been flagged as major risks for hosted MCP servers.

Solution: Self-design and self-host MCPs to keep sensitive data in-house.

Custom MCPs: Tailored for Success

Generic MCPs require heavy prompting and extra logic to steer the agent through the noise.
Custom MCPs flip the script:

• Built for a single purpose
• Tailored to your data source
• Tuned to your agent’s needs

You still stay MCP-compliant for interoperability—but avoid the bloat.

Benefits of Custom MCPs

✅ Clean and Efficient
By designing the MCP server exactly how you want it to act, agentic tools become slimmer—no unnecessary parsing or overhead.

✅ Low Error Rates
Agents aren't overwhelmed by irrelevant features. Built-in chunking for large datasets prevents misreads and crashes.

✅ Enhanced Security and Control
Self-hosted MCPs keep data sovereign and compliant with internal security standards.

The Trade-off

There is development cost involved in building them from scratch.
But with AI-assisted coding tools and low-code platforms, what once took weeks