Top 15 Remote MCP Servers Every AI Builder Should Know in 2026

Just like coding AI models, MCP (Model Context Protocol) servers have evolved, from SDIO, to SEE, to rich HTML streaming, and now to fully remote, OAuth-secured cloud instances.

This evolution marks the end of the old era where you had to manually host MCP servers, configure environment variables, and risk exposing API keys to the system.

Today, remote MCP servers offer secure authorization, zero-leak credential handling, reliable uptime, and significantly faster execution than local setups. The shift isn’t just technical; it fundamentally changes how AI builders work, automate, and scale their agent workflows.

Image by Author

In this guide, we will explore the top 15 MCP servers used daily by AI builders, developers, product teams, and automation engineers.

To make it easy, we have grouped them into four practical categories:

• Developer and infrastructure
• Productivity and workflow
• AI intelligence and memory
• Search and information retrieval

Each MCP server includes:

• Introduction
• Setup command for Claude Code
• Top 7 key features

Our goal is simple: help you move faster, build safer, and operate like the AI engineers defining 2026, not just adapting to it.

If you’re eager to learn more about MCP servers and their uses, I recommend the Building Scalable Agentic Systems course. 

Top Development and Infrastructure MCP Servers

These servers connect AI agents directly to core development workflows, repositories, databases, deployment platforms, and financial systems.

1. GitHub

Instead of digging through repos, branches, and diffs yourself, let your assistant do the navigating. GitHub MCP lets your AI read codebases, surface changes, inspect PRs, and track workflows while staying fully permission-aware. It enables natural language interaction with your entire project while respecting authentication and access controls.

Run the following command in the terminal to set up MCP in Cloud Code:

claude mcp add -s user -t http github https://api.githubcopilot.com/mcp/

Key features:

• Repository Reading: Access files, folders, commits, and project structure.
• Issue and Pull Request Management: Create, update, review, and track development items.
• Workflow Visibility: View run history, logs, build results, and deployment records.
• Security Review: Check alerts, dependency status, and automated vulnerability reports.
• Code Analysis: Summarize functions, review changes, and understand architectural layout.
• Team Collaboration Access: Read discussions, comments, and notification timelines.
• Natural Language Execution: Perform tasks conversationally instead of manually.

2. Supabase

Think of this tool as giving your assistant read-only access to your database brain. The Supabase Remote MCP Server connects your artificial intelligence tools directly to your Supabase projects so they can query data, explore schemas, and test application logic using natural language. It enables secure, scoped interaction with development databases while following project permissions and access rules.

Run the following command in the terminal to set up MCP in Cloud Code:

claude mcp add -s user -t http supabase https://mcp.supabase.com/mcp

After adding the server, authenticate once, and your assistant can ask questions about tables, run structured queries, and explore project metadata through conversational commands.

Key features:

• Database Exploration: View schemas, tables, columns, keys, and relationships.
• Query Execution: Run test queries on your development environment using natural language.
• Project Scoping: Limit access to a single selected Supabase project for controlled data visibility.
• Read Only Mode: Allow safe viewing of data without any write actions or modifications.
• Authentication Support: Connect through browser sign-in or manual token-based authentication when needed.
• CI Environment Compatibility: Use personal access tokens for environments that cannot open browser-based sign-in.
• Developer Focused Safety: Designed for development use only to avoid exposing production-level data.

3. Vercel

Instead of bouncing between dashboards and logs, let your assistant handle the deployment side of things. Vercel MCP gives your AI-controlled access to your projects so it can look up deployments, inspect build logs, and reference documentation, all while staying within your existing OAuth and permission rules.

Run the following command in the terminal to set up MCP in Cloud Code:

claude mcp add -s user -t http vercel https://mcp.vercel.com

After adding the server and authenticating, your assistant can view deployment logs, explore configuration history, and retrieve project information through conversational commands.

Key features:

1. Documentation Search: Navigate official Vercel documentation and find configuration guidance.
2. Deployment Insight: Review build outputs, investigate deployment history, and identify errors.
3. Project Access Control: Connect with OAuth and follow approved client permissions.
4. Team and Project Context: Connect globally or use project-specific URLs for scoped access.
5. Client Compatibility: Works with Claude, ChatGPT, Cursor, Copilot, and additional supported platforms.
6. Secure Authorization Flow: Requires explicit approval to grant tools access to your Vercel account.
7. Human Confirmation Support: Review actions before they run to avoid unapproved changes.

4. Stripe

Managing payments and billing should not involve dashboard juggling. Stripe MCP allows your assistant to pull account context, issue invoices, check balances, and answer subscription questions in plain language, all inside chat. It provides OAuth-based access to your Stripe account with the option to use restricted keys when building automated agents.

Run the following command in the terminal to set up MCP in Cloud Code:

claude mcp add -s user -t http stripe https://mcp.stripe.com

After adding the server and authenticating, your assistant can retrieve balances, create products, list subscriptions, and search Stripe knowledge resources through conversational commands.

Key features:

1. Account and Balance Access: Retrieve account details and view current balance information.
2. Customer and Subscription Actions: Create customers and manage subscription lists and updates.
3. Invoice and Payment Tools: Create invoices, finalize them, and list payment intent records.
4. Resource Search: Search Stripe resources and documentation with natural language input.
5. Secure OAuth Connection: Use OAuth for scoped access or restricted keys for local automation.
6. Autonomous Agent Support: Allow automated tools to perform approved API actions safely.
7. Local Development Option: Run a local server configuration when remote access is not suitable.

Top Productivity and Workflow MCP Servers

These interfaces allow AI to support project planning, UX iteration, collaboration, and enterprise-wide automation.

5. Notion

Your workspace finally becomes visible to your assistant. Notion MCP opens up structured access to pages, comments, and database entries so the AI can retrieve and reference context rather than ask you to paste it. Notion MCP follows your workspace permissions and allows your assistant to retrieve live context while maintaining secure access rules.

Run the following command in the terminal to set up MCP in Cloud Code:

claude mcp add -s user -t http notion https://mcp.notion.com/mcp

After adding the server and completing OAuth, your assistant can read workspace content, surface database entries, and reference page information through conversational commands.

Key features:

1. Workspace Context Access: Retrieve pages, databases, and comment threads.
2. Permission Aligned Retrieval: Only access items based on your existing Notion permissions.
3. Streamable HTTP Support: Connect with the recommended live streaming endpoint for synced updates.
4. Alternative Connection Modes: Configure connection via Server Sent Events or local setup.
5. Directory Integration: Connect directly through Notion’s in-app MCP connector list.
6. Troubleshooting Visibility: Identify missing MCP support or remote connection limitations in your tool.
7. Custom Client Setup: Configure manual JSON connections for tools that do not yet support MCP directories.

6. Linear

Project flow often lives in your head, but tasks live in Linear. This Linear MCP lets your assistant find issues, update tickets, review project status, and keep your backlog moving while staying within your existing permissions.

Run the following command in the terminal to set up MCP in Cloud Code:

claude mcp add -s user -t http linear https://mcp.linear.app/mcp

After adding the server and completing the authentication flow, your assistant can list active issues, review project progress, update ticket fields, and surface comments through conversational commands.

Key features:

1. Issue Interaction: Create, edit, list, and search Linear issues.
2. Project Context: Retrieve project details, statuses, and tracked milestones.
3. Comment Access: Retrieve discussion threads tied to issues and tasks.
4. Streamable HTTP Support: Use the recommended live endpoint for reliable updates.
5. Authentication Alignment: OAuth-based login with dynamic client registration.
6. Multi Client Compatibility: Works with Claude, Cursor, Codex, Visual Studio Code, and Windsurf.
7. Remote Management Safety: Centrally hosted server with secure access to workspace data.

7. Zapier

If your assistant could actually do things, not just suggest them, this is how. Zapier MCP provides real, controlled access to 8,000 apps so AI can automate scheduling, messaging, reporting, and follow-ups on command. It provides a single integration point with access to over eight thousand applications and managed authentication through Zapier.

Run the following command in the terminal to set up MCP in Cloud Code:

claude mcp add -s user -t http zapier https://mcp.zapier.com/api/mcp/mcp

After adding the server and configuring actions in Zapier, your assistant can send messages, create records, schedule events, and perform other live actions through conversational commands.

Key features:

1. Multi App Access: Connect to more than eight thousand applications through a single interface.
2. Action Automation: Trigger supported actions such as posting messages, updating records, or generating events.
3. Prompt to Action Mapping: Convert natural language instructions into accurate application calls.
4. Scalable Integration: Use existing authentication, retry handling, and quota management from Zapier.
5. Custom Tool Selection: Choose exactly which application actions your assistant can perform.
6. Cross Platform Compatibility: Works with Claude, ChatGPT, Cursor, Windsurf, and related MCP-supported tools.
7. Team and Enterprise Support: Connect business systems without building custom application integrations.

8. Figma

Design intent should not get lost in translation. With Figma MCP, your assistant has access to your Figma workspace so it can understand selected frames, extract design context, and align generated code with real components. Figma MCP provides design, FigJam, and Make file information to your assistant while maintaining workspace permissions and rate limits.

Run the following command in the terminal to set up MCP in Cloud Code:

claude mcp add -s user -t http figma https://mcp.figma.com/mcp

After enabling the desktop or remote server, your assistant can retrieve frame data, reference design variables, and generate implementation code through conversational commands.

Key features:

1. Frame to Code Generation: Turn selected frames into structured implementation code.
2. Design Context Extraction: Access variables, components, and layout information.
3. FigJam Resource Access: Retrieve diagram content to support code workflows.
4. Make File Retrieval: Gather Make file context to help in prototype to production transitions.
5. Design System Alignment: Maintain accuracy with components through Code Connect.
6. Local or Remote Operation: Use a local desktop server or connect to the hosted remote endpoint.
7. Workspace Permission Respect: Follow seat limits, rate rules, and access controls set by your plan.

Top AI Intelligence and Memory MCP Servers

These servers enhance agent cognition and memory and provide access to the vast community hosted on the MCP servers on Hugging Face Hub.

9. Hugging Face

The Hugging Face Remote MCP Server lets it browse models, datasets, Spaces, and papers, pulling only what is relevant so you can iterate without leaving your environment. It provides live access to Hub metadata and community tools while following your account permissions.

Run the following command in the terminal to set up MCP in Cloud Code:

claude mcp add -s user -t http huggingface "https://huggingface.co/mcp?login"

After adding the server and signing in, your assistant can search resources, run Spaces, inspect repository details, and perform Hub queries through conversational commands.

If you’re keen to learn more about the Hugging Face ecosystem, I recommend the Hugging Face Fundamentals skill track. 

Key features:

1. Model and Dataset Search: Find machine learning models and datasets with filters for task and authors.
2. Spaces Semantic Access: Discover Spaces and run supported applications from the Hub.
3. Documentation Search: Retrieve relevant documentation pages for guidance and debugging.
4. Job and Task Control: Run, monitor, and manage infrastructure jobs directly.
5. Repository Insight: View repository metadata, tags, and README information.
6. Dynamic Spaces Support: Experiment with runtime calls to Spaces configured as MCP tools.
7. Interface Compatibility: Connect from Claude, Cursor, VS Code, Windsurf, and related MCP clients.

10. Sequential Thinking

Reasoning is rarely linear. The Sequential Thinking MCP Server provides access to your assistant with the structured reasoning engine so it can break complex problems into steps, revise earlier thoughts, branch into alternatives, and converge on better solutions using natural language.

Run the following command in the terminal to set up MCP in Cloud Code:

claude mcp add -s user -t http sequential-thinking https://remote.mcpservers.org/sequentialthinking/mcp

After adding the server, your assistant can request additional thinking steps, revisit previous reasoning, and maintain a numbered chain of thoughts through conversational commands.

Key features:

1. Structured Step Control: Break problems into numbered thought steps with clear progression.
2. Revision and Refinement: Mark steps as revisions and update earlier thinking while keeping history.
3. Branching Reasoning: Create branches from specific thought numbers and explore alternative paths.
4. Dynamic Depth: Adjust the total number of planned thoughts as new information appears.
5. Hypothesis Generation: Propose, refine, and verify potential solutions over multiple steps.
6. Context Preservation: Maintain a consistent reasoning context across many interactions.
7. Multi Client Support: Configure with Claude, VS Code, Codex, Cursor, and other MCP compatible tools.

11. Mem0 (OpenMemory)

Forget re-explaining context every time you switch tools. OpenMemory MCP creates a persistent, private memory layer that stays with you, locally or in a secure hosted environment, so assistants can recall preferences, decisions, and project details without prompting you again. You can read our guide to Mem0 to learn more.

Run the following command in the terminal to set up MCP in Cloud Code:

npx @openmemory/install --client claude --env OPENMEMORY_API_KEY=your-key

After installing and signing in at the hosted dashboard, your assistant can save information, search stored memories, and access shared context across clients through conversational commands.

Key features:

1. Persistent Memory Storage: Add and retrieve long-term context that survives sessions.
2. Local Control Option: Run fully on your machine without cloud sync or external use.
3. Shared Cross-Client Recall: Store information in one tool and access it in another.
4. Unified Memory View: Inspect, delete, and manage stored memory through a single interface.
5. Standard Memory Operations: Use consistent actions such as add, search, list, and delete.
6. Fast One-Click Setup: Use the hosted version without manual server setup or Docker.
7. Multi Client Compatibility: Connect from Cursor, Claude Desktop, Windsurf, and related MCP tools.

Top Search and Information Retrieval MCP Servers

These servers give agents domain-specific access to live documentation, contextual lookup, web search, and structured content extraction. 

12. Tavily

Web search without noise. Tavily MCP gives your assistant targeted, filtered retrieval, so research is fast and factual rather than scroll-heavy and ambiguous. With this MCP, you get access to Tavily’s live web search and extraction engine so they can access fresh online information, filter results, and perform domain-specific lookups using natural language. 

Run the following command in the terminal to set up MCP in Cloud Code:

claude mcp add -s user -t http tavily "https://mcp.tavily.com/mcp/?tavilyApiKey=<your-api-key>"

After adding the server and including your Tavily API key, your assistant can run searches, extract page content, and return structured insights through conversational commands.

Key features:

1. Web Search Access: Retrieve current information across the public web with focused search parameters.
2. Content Extraction: Extract relevant text, summaries, and structured details from search targets.
3. Domain Filtering: Narrow results by topic, source type, or domain preference.
4. Remote Server Option: Connect with a hosted endpoint for quick configuration without local setup.
5. Bridge Support: Use mcp-remote for tools that cannot communicate directly with remote servers.
6. API Key-Based Control: Authorize usage through your personal Tavily key for secure access.
7. Client Compatibility: Connect from Claude Desktop, Cursor, OpenAI tooling, and other MCP-enabled clients.

13. Exa

Documentation, code examples, and library usage direct from the source, not hallucinated. The Exa MCP fetches precise GitHub examples, API snippets, and best practices to keep your coding agent grounded in reality. It enables precise retrieval across repositories, documentation sources, and technical forums to support accurate code generation and research.

Run the following command in the terminal to set up MCP in Cloud Code:

claude mcp add exa -e EXA_API_KEY=YOUR_API_KEY -- npx -y exa-mcp-server

After adding the server and including your Exa API key if required, your assistant can search codebases, retrieve implementation references, and perform live information lookups through conversational commands.

Key features:

1. Code Context Retrieval: Access targeted examples and documentation from real repositories.
2. Real Time Web Search: Gain current results across technical sources and development forums.
3. Selectable Tool Set: Enable only the Exa tools you need for focused performance.
4. Deep Research Mode: Initiate extended research tasks and retrieve full results when ready.
5. Domain Search Support: Perform company research, LinkedIn lookup, and site specific crawling.
6. Hosted Remote Access: Connect to the managed server endpoint without local installation.
7. Client Flexibility: Configure with Claude Code, Cursor, and other MCP-compatible environments.

14. Fetch

When you just need the content, not the browser, Fetch MCP extracts web pages into markdown or raw form in seconds, so your assistant can read rather than render. It enables streamlined content extraction for research or reference without heavy rendering or navigation layers.

Run the following command in the terminal to set up MCP in Cloud Code:

claude mcp add -s user -t http fetch https://remote.mcpservers.org/fetch/mcp

After adding the server, your assistant can fetch URLs, read page sections in chunks, and request either raw HTML or preprocessed markdown formats.

Key features:

1. Live Page Fetching: Retrieve content directly from a specified URL for quick inspection.
2. Markdown Conversion: Automatically convert HTML into clean, readable markdown output.
3. Chunk-Based Extraction: Read long pages by selecting a starting index and returning only what is needed.
4. Adjustable Length: Set maximum returned characters for efficient, token-safe usage.
5. Raw Content Option: Return original HTML when deeper parsing or custom formatting is required.
6. No Browser Overhead: Avoid full browser execution to keep agent pipelines fast and minimal.
7. Direct MCP Access: Connect through a hosted endpoint without installation or local setup.

15. DeepWiki

You can ask questions about entire repos without knowing where to look. DeepWiki MCP returns explanations, structure, and documentation drawn from indexed GitHub sources with no setup barrier.

Run the following command in the terminal to set up MCP in Cloud Code:

claude mcp add -s user -t http deepwiki https://mcp.deepwiki.com/mcp

After adding the server, your assistant can fetch GitHub repositories, read the entire structure and files of the repository, and pull repository analysis.

Key features:

1. Ask Questions: Query entire repositories indexed on DeepWiki.
2. Structured Repo Reading: Retrieve repository tree, folders, and wiki layout.
3. DeepWiki Index Access: Pull curated summaries built from full-repo analysis.
4. Wiki Content Extraction: Read documentation and contextual notes stored in DeepWiki.
5. Public Repo Support: Access any indexed public repository without authentication.
6. Private Repo Support: Add private repositories via Devin with API authorization.
7. MCP Native Integration: Works directly inside Claude Desktop, Cursor, and compatible tools.

Summary

To help you quickly understand the leading remote MCP servers, the table below organizes the top 15 MCP integrations by category, best use case, and usage URL.

Each MCP endpoint can be connected to any AI assistant or coding agent to extend capabilities such as code review, deployment analysis, billing operations, memory persistence, and search.

All listed MCP servers support secure authentication, typically handled through OAuth, so no manual token exchange is required. 

If you’re ready to apply these MCP concepts hands-on, I recommend checking out DataCamp’s Developing AI Systems with the OpenAI API course to start building smarter AI tools.