AI agents no longer just answer questions. They read code, install apps, run workflows, transfer money, search the web, and remember what happened last week. MCP servers make that possible by connecting models to real systems.
MCP replaces scattered APIs with structured, permission-aware access to tools like GitHub, Stripe, Notion, databases, and web search. This article answers one question: which MCP servers are used when building systems in 2026.
1. Working with code
GitHub MCP | The nervous system of your code
GitHub MCP allows your agent to read repositories, check commits, review pull requests, and track issues while remaining fully consent aware. Instead of guessing what your code is doing, AI can actually look.
Your GitHub repository is managed independently with AI. The real development of the agent begins!
It is used
- Code reading and summarizing
- Reviewing pull requests
- Understanding the project structure
- Track issues and changes
GitHub MCP allows AI to behave like a developer instead of just another tool.
2. For shipping products
Vercel MCP | Deployment and uptime accuracy
Vercel MCP lets your agent see what’s really live. Builds, logs, usage, previews, and environment settings are all expressed in natural language.
It is used
- Checking the shipment
- Learning lumber
- Failure to debug
- Understanding the hosting situation
This is where AI jumps from coding to DevOps.
3. Workflow design
Figma MCP | Design the code context
Figma MCP gives your AI agent direct access to your design files. It can read frames, inspect parts, draw exceptions, and understand layouts instead of guessing from screenshots or wavy descriptions by hand.
Product design is no longer limited to human-readable PDFs using Figma MCP.
It is used
- Learning UI architecture
- Extracts design tokens
- Understanding the parts
- Converting frames to code
Ideal for seamlessly integrating AI into design processes.
4. For running businesses
Stripe MCP | Money and payment intelligence
Stripe MCP gives your AI access to balances, customers, invoices, subscriptions, and payment processing. This is where agents start dealing with real income in a secure way.
Gone are the days of online error proneness.
It is used
- Payment visibility
- Subscription management
- Payment tracking
- Income reporting
This is where AI comes into play for real businesses.
5. To stay organized
MCP vision | The mind of your company
Notion MCP allows your agent to read pages, databases, and comments from your workspace. It can see details, roadmap, and decisions without you having to attach them to the conversation.
Notion MCP allows AI to access organizational memory.
It is used
- Reading internal documents
- Downloading information
- Search for company information
- Understanding decisions
This is what turns AI into a partner instead of a tool.
6. Work management
Linear MCP | An execution layer for groups
Linear MCP gives your agent access to issues, milestones, comments, and project status. It can track work and review tickets like a real partner. This allows for automating the meta around the development process.
It is used
- Reading and reviewing problems
- Tracks project status
- Backlog management
- Linking to do
This is how agents stop advising and start acting.
7. For automatic actions
Zapier MCP | AI performance engine
Zapier MCP gives your agent access to thousands of apps. Emails, CRMs, calendars, Slack, spreadsheets. If Zapier can do it, your agent can too.
It is used
- Automated workflow
- Cross-app actions
- Performance of work
- Real world orchestration
This is where the AI leaves the chat window.
8. For information
Tavily MCP | Live the genius of the web
Tavily MCP provides your agent with real-time, focused web search. No scrubbing, no noise. Fresh, factual information when relevant.
It is used
- Research
- Market intelligence
- Fact checking
- Web data extraction
This is how hallucinations are replaced by evidence.
9. For basic answers
Example of MCP | Source-based retrieval
Exa MCP finds original code, documentation, and technical resources on the web. It allows agents to write and interpret things based on evidence, not vibes.
It is used
- Code examples
- API pointers
- Technical research
- Source-based answers
This way the AI becomes trustworthy instead of just self-confident.
10. To stay current
MCP for Hugging Face | AI knowledge center
Hugging Face MCP allows your agent to browse models, datasets, Spaces, and papers from the world’s largest AI community.
It is used
- Finding models
- It examines data sets
- Running spaces
- Reading texts
This is how the agents always work instead of being set on training data.
Final thoughts
The list of MCP servers displayed is obviously incorrect perfect! There are so many out there and more are coming out every week. What matters though is what fits your purpose.
You can use GitHub, allowing an agent to understand your code. Visualize your plans in Notion. Describe your work in Linear. Make use of Vercel. Spend income at Stripe. And your connection to the wider AI ecosystem with Hugging Face.
The list is almost endless. All that’s left is to connect the appropriate MCP server to your AI workflow.
Frequently Asked Questions
A. They give agents structured, permission-aware access to real systems like code, shipping, and payments instead of passive APIs.
A. GitHub, Vercel, Stripe, Notion, and Stripe cover code, deployment, currency, context, and functionality.
A. Tools like Tavily and Exa base answers on live web data and real sources instead of modeled assumptions.
I specialize in reviewing and refining AI-driven research, technical documentation, and content related to emerging AI technologies. My experience includes AI model training, data analysis, and information retrieval, which allows me to create technically accurate and accessible content.
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