When you’re building workflows that rely on multiple SaaS tools, connecting everything—especially AI models—can feel like a tedious patchwork. The Model Context Protocol (MCP) offers a more structured approach to this chaos.
In this article, you will learn what MCP is and how it helps AI models to interact with other tools and perform tasks, without needing a developer to glue things together every time.
Model Context Protocol: Definition
MCP is a protocol that allows AI agents to not just respond to user queries, but also take real actions in external systems through a defined API interface.
It provides a universal, open standard for connecting AI systems with data sources, replacing fragmented integrations with a single protocol. The result is a simpler, more reliable way to give AI systems access to the data they need.
MCP was released by Anthropic, a company that created Claude AI. It is being built as an open-source project. That means anyone can adopt, contribute to, or adapt it to their specific workflows. This is key for startups and SaaS platforms that need flexibility but can’t afford long integration cycles.
If you want to understand better how AI agents work and why they’re important when talking about MCP, check out our guide.
Why MCP matters
Let’s say you're a startup using AI to score leads, generate outreach emails, and sync everything to your CRM.
You need different software intermediaries called APIs to connect AI to all your tools. For example, if you have an AI agent inside your CRM, you need unique APIs for your mailing tool, database, Slack for notifications, etc. If you want to add a new tool, you need to ensure it provides an API you can use ― otherwise, you would have to create it or use an integration platform. For non-techy users, all these can be limiting. But even for more tech-savvy people, connecting all and setting up a system that doesn’t break can still be tedious.
This is where MCP comes in. It introduces a shared protocol for how models receive data, understand their environment, and interact with other tools. A single protocol instead of all that mess.
You can think of it as a universal remote control, where pressing the right buttons is enough. Adding new features or tools is much easier. That means fewer mistakes, faster deployments, and better performance.
Check out our blog post about 40+ AI tools for different niches and use cases.
How it works (without the jargon)
The technology consists of two parts:
- MCP Server. It defines a set of supported API requests, including what methods are available and what parameters they require. Can be developed by anyone, hosted anywhere. Configuration includes instructions for each method—used when the agent connects to the service.
In this GitHub repository, you will find all the available MCP servers. For example, Slack, Google Drive, and Notion already have their own servers.
- MCP Client. Handles the communication between the user and server. Usually embedded inside an AI agent like Cursor. Today, Cursor is one of the few agents that support MCP. But, judging by how important this new technology is, it’s bound to change soon.
When you use an MCP, it looks like this:
- User types a command, for example, create a task in YouTrack.
- Agent processes the prompt. It uses keywords to understand in what tool they need to perform the task (YouTrack) and what command to perform (create task).
- Agent identifies the matching API and parameters from the MCP server.
- The agent builds a ready-to-use API request, and the MCP server executes it. The MCP server then returns the result back to the agent.
Practical use cases
MCP is a very young technology, and there aren’t yet many tangible use cases. But this is how it will probably be used in the future.
Sales automation
In sales, MCP can reduce manual CRM entry and accelerate response time.
Contextual lead creation. Operator writes “create lead” in the CRM/chat—MCP identifies the intent, extracts lead data from conversation, creates the record, and attaches a conversation summary.
Software development automation
MCP can help software developers to minimizes tool-switching and automate routine.
Integrated task actions. In environments like Cursor, devs can input structured requests (like “submit PR for code review”). MCP interprets and executes the action across tools, for example, pushes comments to GitHub.
Marketing automation
MCP provides marketers with an opportunity to reach to a wider audience with minimal input.
Content coordination. Email or Slack message like “schedule product update post for Friday on LinkedIn”—MCP schedules the post using pre-approved templates.
Customer support automation
In customer support, MCP helps to shorten case handling time and standardize reporting.
Case creation and triage. Agent writes “open ticket for refund issue” in chat—MCP auto-generates a support case, classifies the issue, and routes it.
Contextual summary generation. After a support interaction, MCP generates a case summary and attaches it to the ticket.
HR automation
For human resources, this technology can help to ensure consistency and more frictionless operations.
Onboarding workflows. “Onboard new hire: John Smith, start May 1”—MCP initiates standard onboarding tasks across systems (email setup, access provisioning, welcome email).
Time-off request handling. “Request PTO May 10–15” in chat—MCP logs the request, checks calendar conflicts, and notifies manager.
Benefits of Model Context Protocol
MCP makes it easier for non-technical teams to use pre-built automations and connect tools without touching code.
Reduced complexity. Whether you're integrating a lead enrichment tool, syncing emails, or building something more agentic (where AI tools act semi-independently).
Improved user experience. It’s invaluable for platforms offering drag-and-drop workflows. Once MCP is in place, users can mix and match models, data sources, and actions without thinking about backend logic.
Limitations of MCP
While MCP can support your automations and reduce manual work, it also has certain limitations.
Single-action only. MCP agents currently support only one command at a time. No conditional logic or multi-step workflows yet.
No repetitive action support. If you need to set up an automation that will be executed every 24 hours you still need to use an iPaaS platform.
Limited complexity. AI is a black box―sometimes it misinterprets the context and it’s hard to pinpoint what went wrong. If you set up workflows that are complex, it may create chaos that won’t be easy to resolve.
Security and control. No token or cost usage limits. No access logs—hard to trace what the AI did and how. No safeguards if AI misinterprets intent.
Summing up
AI in SaaS is moving fast. But without a shared standard, each integration becomes a one-off effort. Model Context Protocol changes that. It offers a reliable way to connect models, apps, and data—saving time and reducing failure points.
MCP opens the door to faster, smarter, and more maintainable AI workflows for founders, marketers, and product teams working in the no-code space.
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