About MCP's - What you need to know about Model Context Protocol

​After AI agents entered the hype block, Model Context Protocol (MCP) is the next real unlock.

MCP is an open standard that lets AI models interact directly with external tools, APIs, and databases. Instead of relying on custom integrations, MCP provides a structured way to give AI real-world access while keeping security and efficiency in check.​

Why MCP matters

1. Standardized Access – AI can connect to different systems without custom APIs.​
2. Flexible Implementation – Supports Python, TypeScript, Java, and Kotlin.​
3. Security First – Keeps data within your control while enabling AI automation.​

How MCP Works

MCP uses a client-server model where AI (client) requests data or actions from an MCP server, which handles the connection to various tools. It communicates via JSON-RPC over HTTP or WebSockets, making interactions efficient and scalable.​

Top Use Cases

File Management

Scenario: An AI assistant needs to summarize recent reports stored in a company's file system.​

Solution: Using an MCP server connected to the file system, the AI can read the necessary files, extract key information, and generate summaries.​

Database Interaction

Scenario: A business analyst wants to retrieve sales data from a PostgreSQL database to identify trends.​

Solution: Through an MCP server with read-only access to the database, the AI can execute queries, process the data, and present insights.​

Development Tools Integration

Scenario: A developer seeks to automate code reviews by analyzing pull requests on GitHub.​

Solution: An MCP server interfacing with the GitHub API allows the AI to fetch pull request details, review code changes, and provide feedback.​

Web Automation

Scenario: A researcher needs to gather information from multiple websites for a market analysis.​

Solution: By leveraging an MCP server equipped with web scraping capabilities, the AI can navigate websites, extract relevant data, and compile reports.​

Productivity and Communication

Scenario: A team wants to monitor and summarize discussions from their Slack channels.​

Solution: An MCP server connected to Slack enables the AI to access channel messages, identify important topics, and generate summaries for the team.​

Expanding MCP Integrations

Beyond the common use cases, MCP's versatility extends to various other tools and services:​

Git Integration

Scenario: A development team needs to manage and automate their Git workflows.​

Solution: An MCP server can interact with Git repositories, facilitating operations like cloning, committing, and pushing changes.​

GitHub

MongoDB Access

Scenario: A data scientist requires access to unstructured data stored in MongoDB for analysis.​

Solution: An MCP server can interface with MongoDB, allowing the AI to query and process the data effectively

GitHub

Docker Management

Scenario: An operations team wants to automate the deployment of applications using Docker containers.​

Solution: An MCP server can manage Docker operations, enabling the AI to handle container orchestration tasks.​

Figma Design Retrieval

Scenario: A design team needs to extract and analyze design elements from Figma for a project review.​

Solution: An MCP server can connect to Figma, retrieve design data, and present it for analysis.​

HuggingFace Spaces Interaction

Scenario: A machine learning engineer wants to utilize models hosted on HuggingFace Spaces for inference tasks.​

GitHub

Solution: An MCP server can interface with HuggingFace Spaces, allowing seamless integration of these models into the AI's workflow.​

MCP gives AI the missing link to act on real-world data efficiently. It’s open, scalable, and built for automation. Expect more startups to build on it. Full list of MCP's currently available today 👉 Github