Let’s be honest, most AI (Artificial Intelligence) tools today are relatively smart but not always super useful. You ask them to explain something, write an email, maybe generate some code, but the moment you want them to do something real, like schedule a meeting, file a bug report, or move stuff across your tools, they just stare blankly.
That’s where MCP comes in.
MCP stands for Model Context Protocol. It’s a standard that lets AI models talk to external tools like your calendar, task manager, CRM, Notion, or Slack in a structured way. It was first introduced by Anthropic (the folks behind Claude) in late 2023.
Here is a simple way to think about it…
Right now, using AI is kind of like having a great assistant who can't actually take action on your behalf. You ask them to "Schedule a call with Sarah," and they respond with "That's a great idea!" but then don't lift a finger.
MCP is like giving that assistant the keys to your digital workspace, allowing them to access your calendar, notes, and workflows. With this newfound access, they can not only understand your request but also execute it securely and with a deep understanding of the context.
What is MCP?
MCP (Model Context Protocol) is an open protocol for connecting AI systems to external tools and data sources. In essence, it standardizes how an AI can request information or actions from other software. Instead of building a custom integration for every app, developers can use MCP as a common language between the AI and those systems.
Some of the key goals of MCP include:
- Universality: It’s open, not locked to Claude. Any AI or tool can adopt it.
- Data and tool access: AI gets access to up-to-date, live data.
- Security and control: You decide what tools or data the AI can see or act on.
- Stateful interaction: The AI can maintain context across a session, making it behave more like a real assistant and less like a one-off command processor.
How does MCP work?
MCP follows a client-server architecture:
- MCP server: Exposes data and actions in a structured way (like Google Drive files or Jira tickets).
- MCP client: Used by the AI model to communicate with servers and carry out tasks.
- Host application:The actual app you use (e.g., Claude chat, your IDE, internal dashboard).
- Local Data Sources:Your computer’s files, databases, and services that MCP servers can securely access.
- Remote Services: External systems available over the internet (e.g., through APIs) that MCP servers can connect to.
MCP's use of JSON-RPC creates a real-time conversation between the AI and the server. The AI can ask questions, and the server can respond or even initiate updates. This ongoing connection keeps the AI in the loop, allowing it to stay on top of what's happening.
It also means you can have multi-step tasks, like:
- Find a document
- Summarize it
- Create a follow-up task
...all while keeping context.
What can the AI access?
MCP supports three main capabilities: Resources, tools, and prompts. In this section, we will go through each of these capabilities.
1. Resources
Resources are read-only pieces of information that AI can pull in and use, such as:
- Files
- Database records
- Emails
- Notes
Example: The AI sees a PDF report and pulls its content to generate a summary. These are usually user-selected to avoid privacy issues.
Resources are identified using URIs that follow this format:
[protocol]://[host]/[path]
Examples:
- file:///home/user/documents/report.pdf
- postgres://database/customers/schema
- screen://localhost/display1
Servers can define their own custom URI schemes. Resources can contain two types of content:
- Text resources: UTF-8 encoded text like source code, logs, configuration files, plain text, JSON, or XML.
- Binary resources: Base64 encoded binary files like images, PDFs, audio, or video.
Example using Python:
app = Server("example-server")
@app.list_resources()
async def list_resources() -> list[types.Resource]:
return [
types.Resource(
uri="file:///logs/app.log",
name="Application Logs",
mimeType="text/plain"
)
]
@app.read_resource()
async def read_resource(uri: AnyUrl) -> str:
if str(uri) == "file:///logs/app.log":
log_contents = await read_log_file()
return log_contents
raise ValueError("Resource not found")
# Start server
async with stdio_server() as streams:
await app.run(
streams[0],
streams[1],
app.create_initialization_options()
)
2. Tools
Tools are a powerful primitive in MCP that allow servers to expose executable functionality. These can be anything from running a shell command to invoking a third-party API, such as:
- Creating a calendar event
- Sending a message
- Filing a Jira ticket
They are model-controlled, meaning the AI model can automatically decide to call them (with user approval, if needed).
Overview:
- Discovery: Clients can find tools via
tools/list - Invocation: Tools are executed using
tools/call - Flexibility: Tools can be simple math or complex workflows
Unlike resources (which are passive), tools are active and can change the world. Each tool is described with a JSON schema. For example:
{
"name": "githubcreateissue",
"description": "Create a new issue on GitHub",
"inputSchema": {
"type": "object",
"properties": {
"title": { "type": "string" },
"body": { "type": "string" },
"labels": { "type": "array", "items": { "type": "string" } }
},
"required": ["title", "body"]
}
}
This tells the AI exactly what fields it needs to provide when it wants to create an issue.
3. Prompts
Prompts in MCP are predefined templates that guide the AI through specific tasks, such as:
- Onboarding a new hire
- Setting up a sales report
- Writing a changelog from recent commits
The AI doesn’t just invent these, the system provides them. The user can trigger one, and the AI follows the flow. They're often shown as UI elements like slash commands or buttons. They can: Accept dynamic arguments Include context from resources Chain multiple interactions Guide specific workflows
Each prompt is defined with:
{ "name": "string", "description": "string", "arguments": [ { "name": "string", "description": "string", "required": true } ] }
Prompts help standardize and simplify how users interact with AI systems through structured flows.
Why this is better than old school integrations
Traditional AI tools typically rely on built-in static knowledge (which gets outdated fast), or custom-coded integrations (which are a pain to maintain).
MCP gives you live data and actions, all standardized and all under your control.
Because it’s open, other people can build servers (integrations) and any AI client that supports MCP can use them, like adding a new USB-C device to your laptop, and it just works.
Final thoughts
At Civo, we’re building relaxAI to be more than just a chat interface, it’s designed to support real workflows, integrate securely with your tools, and respect your data boundaries. We're aligning it with emerging standards like MCP to ensure it's extensible, future-ready, and developer-friendly. Whether you're working with cloud infrastructure or internal systems, relaxAI aims to act with context, not just respond to prompts, making it a practical assistant for modern teams.
MCP is a huge leap forward in making AI actually useful. It's what lets AI go from just talking to actually doing.
By standardizing how AI connects to tools and data:
- Developers save time on integrations
- Users get more relevant and powerful AI experiences
- Everyone stays in control of what the AI can access or do
Think of MCP like the thing that unlocks your AI assistant’s hands, so it can stop just giving advice and help get things done. As adoption grows, expect to see AI become more embedded in your daily tools, not by taking over, but by working with you, in context, through standards like MCP.
Learn more about MCP here: https://modelcontextprotocol.io/introduction
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