The ONLY MCP Guide You’ll Ever Need 2025
AI agents have become pretty good at thinking, answering questions, writing code and summarizing documents. But the moment you ask them to do something in the real world, they hit a wall.
Want to save a file?
Open a spreadsheet?
Pull data from GitHub?
Trigger a workflow?
Every tool needs a custom integration. Every action needs a developer. Even small changes require rewiring everything.
This is the problem MCP solves.
And because we’re working on Quash MCP, this blog is meant to be THE reference for anyone who needs a simple, reliable explanation of what an MCP really is and how it works.
Let’s break it down.
Why MCP Exists and What It Is
Today, AI agents know how to reason, but they don’t know how to operate tools. If an agent wants to do something like save a file, fetch sales data, update a task — it needs a custom integration written specifically for that tool’s API.
Do that once? Fine. But doing it for dozens of tools? It becomes unmanageable.
MCP or Model Context Protocol exists to fix this.
MCP is a universal language that lets AI agents plug into tools, data, and services without custom wiring. It replaces one-off APIs with a standard way to talk to everything. It was created by Anthropic (the team behind Claude), and it’s already becoming the default way agents interact with the real world.
Instead of building a new integration for every new tool, an MCP-compatible agent can connect to any MCP server and instantly understand:
what actions the tool supports
what inputs it needs
how to call it
how to read the output
Agents go from answering questions → to performing real tasks.
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The Core Pieces of MCP
Even though “protocol” sounds heavy, MCP is made of four simple parts.
MCP Host
This is where the AI agent lives. Examples:
Claude Desktop
IDEs like Cursor
Testing engines like (soon) Quash MCP
The host is the “home base.”
MCP Server
Think of this as an adapter for a real tool. It exposes what the tool can do like:
read/write files
list GitHub PRs
transcribe a YouTube video
query a database
fetch API data
It translates AI-friendly instructions into real actions.
Servers also:
Announce their capabilities
Run the requested actions
Format the results in a way the AI understands
Return errors cleanly when something breaks
MCP Client
This lives inside the agent. It handles:
Discovering servers
Sending requests
Receiving structured responses
Keeping the communication clean
Agents don’t talk to tools directly, they always talk through the client.
The MCP Protocol
This is the rulebook. It defines:
What messages look like
How requests should be structured
How results should be returned
All in simple, predictable JSON. Because everyone follows the same protocol, agents can connect to new tools without custom logic or prior knowledge.
How MCP Works
Here’s a typical flow, you’ll see how clean it is.
AI wants to perform an action. Example: “Save this summary as a text file.”
MCP client checks which servers are available. Maybe there’s a File server.
Server declares its capabilities. “I can read, write, list, delete.”
Agent sends a structured request.
write_file(name: "summary.txt", content: "...")Server executes it. It writes the file to your system.
Server responds with structured output.
{ "status": "success", "path": "/desktop/summary.txt" }Agent continues the next step Maybe emailing it, uploading it, or creating a report.
That’s it. No custom APIs. No manual integration. No hacks. This clean flow is why developers are adopting MCP so quickly.
What You Can Build With MCP
MCP opens up a wide range of possibilities:
Local Tools
file system access
desktop apps
local databases
internal scripts/workflows
Cloud Tools
GitHub
Notion
Google Drive
internal APIs
SaaS apps
Product Workflows
Automations
Data Retrieval
Research Agents
Document Processing
Enterprise Workflows
Code Assistants
Testing & QA
Test data management
Mobile device operations
Structured test flows
Custom debugging actions
If a tool can be exposed as an MCP server, an agent can use it instantly.
MCP vs Traditional APIs
Aspect | Traditional APIs | MCP |
Setup | Manual, one-by-one integrations | One standard for all tools |
Flexibility | Tool-specific rules | Dynamic, schema-based |
Reuse | Hard to reuse across agents | Easily reused everywhere |
Scalability | Breaks as systems grow | Built to scale |
Agent Compatibility | Needs custom logic for each tool | Works out-of-the-box via schema |
Tool Discovery | Manual configuration | Automatic and real-time |
MCP doesn’t replace APIs, it sits above them and standardizes how agents use them.
Why MCP Matters
AI agents are reaching a point where reasoning is not the bottleneck anymore. Their limitation is access.
They need:
A safe way to use tools
A consistent interface
Predictable schemas
Clear capabilities
Real-time communication
MCP gives them all of that.
That’s why:
New MCP servers are being released every week
IDEs and developer tools are adopting it
Companies are building internal servers
Open-source libraries are growing fast
It’s becoming the backbone of “agent → real world” interactions.
How Quash MCP Fits Into This
Quash already has a mobile-use agent that can open apps, scroll, tap, type, validate flows, and manage real mobile execution.
With MCP support coming soon:
Teams can connect their internal tools
Call their test data
Pull custom configs
Integrate internal dashboards
Extend the agent’s abilities in a standard way
Quash becomes both:
an MCP server (exposing mobile actions), and
an MCP client (consuming user tools like test data, APIs, configs).
This gives developers a clean, standardized way to build on top of Quash without waiting for native integrations.
Think of MCP as a universal socket. Any tool can plug in, any agent can use it with no custom adapters. No rewiring when things change.
MCP simplifies one of the biggest issues in AI today: connecting agents to real tools in a predictable way. Whether you’re building agents, tools, workflows, or entire platforms, MCP is the cleanest interface we’ve seen so far. And with Quash MCP coming soon, developers working with mobile testing will finally get a simple, standards-based way to plug their data, tools, and internal workflows directly into the agent.
FAQs
Do I need to be technical to use MCP?
Not really. Using MCP-enabled tools is simple. Building servers requires some coding, but using them does not.
Is MCP only for Claude?
No. MCP is an open standard. Any agent can adopt it and many already have.
Does MCP replace APIs?
No. APIs still exist under the hood. MCP just makes them usable for AI agents in a consistent way.
How is MCP different from function calling?
Function calling is tool-specific. MCP is tool-agnostic, server-discoverable, schema-driven, and reusable across agents.
Is MCP safe?
Yes. Servers define exactly what actions are allowed. Agents cannot access anything beyond what a server explicitly exposes.
Can I build my own MCP server?
Yes. That’s the whole point. Anyone can expose their tool’s capabilities through a server.
Can MCP run locally and in the cloud?
Both. You can expose local files or connect to remote SaaS tools, following the same protocol.
Is this relevant for developers working with agents today?
Absolutely. If you’re building anything with agents, MCP is quickly becoming the standard you’ll rely on.
