What is MCP (Model Context Protocol)? And why it matters for founders

Not so long ago, AI tools felt like smart assistants. You asked a question, got a response, maybe copied it into a document or tool, and moved on. But there was a gap between getting that answer and all the steps you still needed to complete to take action. But now that gap is shrinking. 

Now, founders are moving from chat interfaces to AI systems that actually execute tasks. Instead of prompting five tools and stitching results together, you can describe an outcome and let the system help coordinate the steps.

This shift is being powered by a few underlying changes. One of the most important is something called Model Context Protocol, or MCP, which was introduced in 2024.

MCP is how AI connects to real tools, data, and workflows. Think about how APIs gave developers a standard way to interact with software; MCP is starting to do the same for AI.

Model Context Protocol (MCP) is a standard way for AI models to access tools and context outside of how you trained them. 

MCP lets an AI system understand what tools it can use, what those tools do, and how to use them. Instead of hardcoding integrations or manually wiring every workflow, MCP creates a shared interface between models and the systems around them.

Here’s a useful way to think about it: 

• APIs let developers wire software to software. 
• MCP lets you expose tools to AI in a way that any compatible client can pick up.

With APIs, a developer writes code to call endpoints. With MCP, the model itself can discover available actions and determine when they may be relevant to a request. To make that work, three core pieces come together:

1. Tools: the functions that let your model to perform actions
2. Resources: the data or content the models needs to act
3. Prompts: the instructions you give the model to guide its actions

Together, these building blocks give AI something it hasn’t really had before — a standard way to call functions, read data, and trigger actions in the systems they’re connected to.

If you’re asking, “What is an MCP server?,” here’s the simplest definition: An MCP server is a system that exposes tools in a structured way, so an AI model can use them.

You can think of it as a menu of actions the model can choose from. Each tool has a clear definition. It specifies:

• What the tool does
• What inputs it expects
• What output it returns

For example, an MCP server for finance operations might expose tools like:

• generate_report
• fetch_transactions

Or in a growth context:

• pull_campaign_data
• summarize_performance

Instead of guessing what’s possible, the model can “see” these tools and decide what to call based on the user’s request.

That’s the key difference between how AI used to work and what you can do with MCP. You’re no longer limited to just writing rules. Now, you’re able to give the model a set of capabilities and allow it to select and use tools based on the user’s request.

Once you strip away the jargon, the MCP flow is actually straightforward. Here’s what it looks like in action:

1. A user makes a request to the LLM.
2. A client brokers the connection to the MCP server. (Clients include (such as Cursor, Claude Code, and others.)
3. The MCP server provides a list of available tools.
4. The LLM selects which tool to call based on the request. 
5. The tool executes and returns structured output.
6. The model uses that output to continue or complete the task.

You can picture it like this:

User → Client → MCP Server → LLM → Tool(s) → LLM → Output → Result

What’s happening behind the scenes is simple but powerful. The model helps coordinate actions across tools, instead of just generating text.

For instance, suppose you want your LLM to summarize the latest transactions and flag anything unusual: 

1. First, the LLM will check the MCP server to see available tools. 
2. Then, the LLM will decide which tools to call, such as fetch_transactions and analyze_spend. 
3. It’ll call the tools in sequence and receive structured data back.
4. Then, it’ll compose a final answer to you based on real inputs.

MCP can change how founders can build and run their business and provide leverage.

AI can act as an operator, not just an assistant

Without MCP, AI suggests what to do. With MCP, AI can help execute parts of the workflow. That means fewer manual steps between insight and execution.Instead of having the model pull data, exporting it, and writing a summary, you can actually ask for the outcome.

You can reuse your existing tools

You don’t need to rebuild your stack to make it AI-compatible. If your systems can expose actions through an MCP server, the model can start using them. Your CRM, finance tools, internal scripts, and more can join a shared interface.

You can automate across your stack 

Often, companies’ early automation breaks down across tools. For example, one system might hold data, another triggers actions, and perhaps a third is where results live. Connecting these tools could mean using scripts or performing manual work. But MCP gives the model a way to move across those boundaries more naturally, without those hurdles.

Internal tools can be used by more of your team members

Early-stage teams often have internal tools that only a few people understand. With MCP, those tools can be accessed through simple instructions. That means the number of people who can use these tools and how often they’ll use them will likely increase.

You build leverage without adding headcount

Instead of hiring for every operational gap, you can design systems that can handle more of the work.

Whether you’re using GitHub reference servers, Linear, Cloudflare, or other software, the best way to understand model context protocol use cases is to see how they show up in real workflows.

Finance operations

You could prompt, “Generate our monthly burn report and flag unusual spend.”

Then, behind the scenes:

• The model calls fetch_transactions.
• Then, categorize_expenses.
• Then generate_report.

In the end, you’ll get a real, finished output, and not just a checklist.

Growth and marketing

You could ask, “Summarize last week’s campaign performance and highlight what changed.”

The system could then:

• Pull campaign data
• Compare it to prior periods
• Generate a structured summary

With this approach, you don’t have to click through dashboards trying to understand. The system will help to generate the output for you based on your data. 

Internal tooling

You could prompt, “Find all customers at risk of churn and assign follow-ups.”

The model will then:

• Query your CRM
• Apply simple logic
• Initiate tasks based on designed workflows

With this approach, workflows that used to live in playbooks can be triggered and executed more efficiently.

Developer workflows

You could prompt, “Deploy the latest build and notify the team if anything fails.”

The model could then:

• Run scripts
• Monitor output
• Send updates

This is where MCP starts to blend into command-line interface-based workflows as well.

MCP doesn’t matter for every team yet.

It matters if:

• You’re building AI workflows or agents.
• You have internal tools you want AI to use.
• You’re automating multi-step processes.

It matters less if:

• You’re just experimenting with prompts.
• You don’t have real workflows yet.
• Your systems aren’t connected in any meaningful way.

In other words, MCP is useful when there’s real work to orchestrate.

Here’s a good guideline: If a task requires you to jump between tools, copy information, or repeat the same steps each week, it’s a strong candidate for MCP-driven automation.

Because the protocol is still new, MCP is often misunderstood. Here are the most common misconceptions. 

Misconception: “MCP replaces APIs”

MCPs don’t replace APIs. APIs still power the underlying systems. MCP sits on top, giving AI a structured way to use them.

MCP servers typically wrap APIs. So, the protocol is a layer above, not a substitute. Without MCP, every AI app rebuilds its own integrations. With MCP, a tool author writes one server and any compatible client can use it. This is why MCP matters.

Misconception: “You need to rebuild everything”

You don’t need to rebuild everything. Most of the time, you’re exposing what already exists, not starting from scratch.

Misconception: “MCPs are only for big companies”

Early-stage teams often actually have an advantage compared to big companies because their systems are simpler. There’s just less legacy to work around when adopting MCP.

MCP is still in early stages, but adoption is accelerating. If you zoom out, it’ll probably look familiar. There was a time when APIs were optional, and now they’re expected. 

MCP is on a similar path as APIs. You don’t need to implement it immediately, but understanding MCP now can change how you’ll design systems for the future. 

For instance, you can start thinking in terms of capabilities, not just interfaces. You may also plan to build tools that can be reused by both humans and AI, and design workflows that can be delegated over time.

The founders who benefit the most will approach LLMs and MCPs as a way to rethink how work gets done across their company.

Finance is one of the most structured and repeatable parts of a business. That makes it a strong candidate for MCP.

Imagine simply prompting:

• “What’s our current burn and runway?”
• “Flag any unusual transactions from the last week.”
• “Prepare a report for investors using the latest data.”

With the right setup, those questions and requests can be handled by your LLM and MCP workflow. This is where tools like Mercury fit into the picture. When your financial workflows are already clean and connected, it becomes much easier to layer automation on top.

Ready to get started? Here’s how to connect to the Mercury MCP here.

AI-generated outputs may vary and should be reviewed for accuracy before taking action.