25 Hands-On MCP Projects for AI Engineers
- Codersarts
- Jun 10
- 6 min read
The Model Context Protocol (MCP) is transforming how AI applications interact with external tools and data sources, offering a standardized, secure, and scalable way to connect large language models (LLMs) to platforms like GitHub, Notion, Slack, and more. Whether you're a beginner exploring AI integration or an enterprise architect building complex workflows, hands-on projects are the best way to master MCP.
At Codersarts, our MCP Expert Help service guides developers through every step of the journey. Below, we present 25 hands-on MCP projects, categorized by skill level, to help you build practical skills and unlock the full potential of MCP. Each project includes a brief description, key MCP concepts, and tools involved, empowering you to create context-aware AI solutions.
Beginner-Level MCP Projects (1-10)
Perfect for developers new to MCP or AI integration, these projects focus on understanding the basics of MCP’s client-server architecture, JSON-RPC framework, and tool discovery.
Hello World with MCP
Description: Build a simple MCP server that connects an LLM (e.g., Claude) to a local text file to read and summarize content.
Key Concepts: MCP server setup, stdio transport, basic JSON-RPC requests.
Tools: Python, Claude, local file system.
Outcome: Learn how MCP enables AI to access external data.
To-Do List AI Assistant
Description: Create an MCP server to connect an LLM to a simple to-do list app (e.g., Todoist API) to add and retrieve tasks.
Key Concepts: API integration, MCP client setup.
Tools: Python, Todoist API, any LLM.
Outcome: Understand how MCP facilitates API-based tool interactions.
GitHub Issue Summarizer
Description: Use an MCP server to fetch GitHub issues and have an LLM summarize them.
Key Concepts: OAuth 2.0 authentication, MCP server configuration.
Tools: Node.js, GitHub API, Claude.
Outcome: Practice secure API access with MCP.
Slack Message Responder
Description: Build an MCP server to integrate an LLM with Slack, enabling it to respond to messages in a channel.
Key Concepts: WebSockets transport, real-time interactions.
Tools: Python, Slack API, LLaMA.
Outcome: Explore real-time MCP communication.
File Metadata Extractor
Description: Create an MCP server to extract metadata (e.g., file size, type) from local files and feed it to an LLM for analysis.
Key Concepts: File system access, MCP tool discovery.
Tools: Python, local file system, GPT.
Outcome: Learn how MCP handles local data sources.
Weather Data AI Agent
Description: Connect an LLM to a weather API (e.g., OpenWeatherMap) via an MCP server to provide weather updates.
Key Concepts: API integration, JSON-RPC responses.
Tools: TypeScript, OpenWeatherMap API, Claude.
Outcome: Build a simple AI agent with MCP.
Notion Page Summarizer
Description: Use MCP to connect an LLM to Notion, summarizing pages or extracting tasks.
Key Concepts: Notion API, MCP server setup.
Tools: Python, Notion API, any LLM.
Outcome: Understand MCP’s role in content management integration.
Basic Calculator Tool
Description: Create an MCP server that allows an LLM to perform basic calculations (e.g., addition, multiplication) via a custom tool.
Key Concepts: Custom tool creation, MCP client-server interaction.
Tools: JavaScript, any LLM.
Outcome: Learn to define custom MCP tools.
Google Drive File Reader
Description: Build an MCP server to fetch file contents from Google Drive and pass them to an LLM for summarization.
Key Concepts: OAuth 2.0, Google Drive API.
Tools: Python, Google Drive API, Claude.
Outcome: Master cloud-based integrations with MCP.
Email Subject Generator
Description: Create an MCP server to connect an LLM to an email client (e.g., Gmail API) to generate subject lines based on email content.
Key Concepts: Email API integration, MCP security.
Tools: Python, Gmail API, GPT.
Outcome: Explore MCP’s application in email automation.
Intermediate-Level MCP Projects (11-18)
These projects are designed for developers comfortable with MCP basics, focusing on more complex integrations, multiple tools, and real-world applications.
Automated Code Reviewer
Description: Build an MCP server to integrate an LLM with GitHub for automated code reviews and PR comments.
Key Concepts: GitHub API, MCP tool orchestration.
Tools: Python, GitHub API, Claude.
Outcome: Create an AI-powered coding assistant.
Dynamic Database Query Agent
Description: Connect an LLM to a PostgreSQL database via an MCP server to execute and summarize SQL queries.
Key Concepts: Database integration, secure query handling.
Tools: Python, PostgreSQL, LLaMA.
Outcome: Learn MCP’s role in database-driven AI.
Slack-Notion Task Sync
Description: Create an MCP server to sync tasks between Slack and Notion, with an LLM prioritizing tasks.
Key Concepts: Multi-tool integration, real-time sync.
Tools: Python, Slack API, Notion API, Claude.
Outcome: Build a cross-platform AI workflow.
AI-Powered Calendar Scheduler
Description: Use MCP to connect an LLM to Google Calendar, enabling it to schedule events based on natural language input.
Key Concepts: Calendar API, natural language processing.
Tools: Node.js, Google Calendar API, GPT.
Outcome: Automate scheduling with MCP.
Real-Time Stock Market Assistant
Description: Build an MCP server to fetch real-time stock data (e.g., Alpha Vantage API) and have an LLM provide insights.
Key Concepts: Real-time API integration, data parsing.
Tools: Python, Alpha Vantage API, Claude.
Outcome: Create a financial AI agent.
Jira Ticket Manager
Description: Connect an LLM to Jira via an MCP server to create, update, and summarize tickets.
Key Concepts: Jira API, MCP server scalability.
Tools: TypeScript, Jira API, LLaMA.
Outcome: Streamline project management with MCP.
AI Content Editor
Description: Use MCP to connect an LLM to Google Docs, enabling real-time content editing and suggestions.
Key Concepts: Google Docs API, collaborative editing.
Tools: Python, Google Docs API, Claude.
Outcome: Build an AI-powered content creation tool.
Custom CRM Integration
Description: Create an MCP server to connect an LLM to a CRM (e.g., Salesforce) for lead analysis and follow-up suggestions.
Key Concepts: CRM API, secure data handling.
Tools: Python, Salesforce API, GPT.
Outcome: Enhance CRM workflows with AI.
Advanced/Enterprise-Level MCP Projects (19-25)
These projects are for experienced developers and enterprises aiming to build sophisticated, scalable, and secure MCP-based solutions.
Multi-Tool AI Workflow Orchestrator
Description: Build an MCP server to orchestrate interactions between an LLM and multiple tools (e.g., GitHub, Slack, Notion) for a unified workflow.
Key Concepts: Tool orchestration, MCP scalability.
Tools: Python, multiple APIs, Claude.
Outcome: Create a centralized AI agent hub.
Secure Enterprise Data Connector
Description: Develop an MCP server to connect an LLM to an internal enterprise database with strict governance and role-based access.
Key Concepts: OAuth 2.0 scopes, enterprise security.
Tools: Java, internal database, LLaMA.
Outcome: Build a secure AI data pipeline.
AI-Driven Customer Support Agent
Description: Use MCP to integrate an LLM with Zendesk and internal knowledge bases to provide real-time customer support.
Key Concepts: Multi-source integration, real-time responses.
Tools: Python, Zendesk API, Claude.
Outcome: Automate enterprise-grade support.
3D Modeling Assistant with Blender
Description: Create an MCP server to connect an LLM to Blender for AI-assisted 3D modeling tasks (e.g., generating scripts).
Key Concepts: Custom tool integration, creative AI.
Tools: Python, Blender API, GPT.
Outcome: Explore MCP in creative applications.
Real-Time Analytics Dashboard
Description: Build an MCP server to connect an LLM to a BI tool (e.g., Tableau) for real-time data visualization and insights.
Key Concepts: BI tool integration, data streaming.
Tools: TypeScript, Tableau API, Claude.
Outcome: Create an AI-powered analytics platform.
Cross-Platform AI Agent
Description: Develop an MCP server to enable an LLM to operate across multiple platforms (e.g., Slack, Jira, Google Drive) for end-to-end automation.
Key Concepts: Cross-platform orchestration, MCP modularity.
Tools: Python, multiple APIs, LLaMA.
Outcome: Build a versatile AI agent.
Custom MCP Server for Proprietary Tools
Description: Create a fully custom MCP server to integrate an LLM with a proprietary enterprise tool or legacy system.
Key Concepts: Custom protocol implementation, enterprise integration.
Tools: Java/C#, proprietary system, Claude.
Outcome: Enable AI for legacy systems.
Why Build These Projects with Codersarts?
Mastering MCP requires hands-on experience, but you don’t have to do it alone. Codersarts’ MCP Expert Help service provides:
Guided Project Support: Our experts mentor you through each project, from setup to deployment.
Custom Solutions: Need a project tailored to your business? We’ll design and implement it for you.
Training & Resources: Access tutorials, code samples, and training sessions to accelerate your MCP learning.
Enterprise Expertise: For advanced projects, we ensure scalability, security, and compliance with enterprise standards.
Get Started Today
Ready to dive into these MCP projects and transform your AI applications? Reach out Codersarts via contact@codersarts.com to explore our MCP Expert Help service.
Whether you’re a beginner building your first MCP server or an enterprise architect designing complex AI workflows, Codersarts is your partner in mastering MCP.
Start your MCP journey today and build the future of AI integration with Codersarts!
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