Top 10 Best MCP Servers to Improve Your AI Workflows

Types of MCP Servers

Before going into more detail, it’s worth defining and clearing up the types of MCP servers out there. You should get familiar with these categories as it will help you narrow down the right fit for your workflow:

• Official Servers: Maintained by companies or organizations, like Oxylabs, Github, or Postman. These offer high reliability, robust documentation, and stable updates.

• Community Servers: Open-source and flexible, like Playwright, Memory Bank, or Sequential Thinking MCP. These ones are ideal for experimentation and customizable implementations, but often require more technical setup and community support.

• Specialized Servers: Purpose-built for specific workflows, such as Claude Code, Cursor IDE, or enhanced search capabilities.

Top 10 Best MCP Servers

1. Oxylabs MCP server

Category: Web scraping & public data access

Best For: AI agents, RAG pipelines, and enterprise data workflows

Official/Community: Official

Price: From $49/month

Setup Difficulty: 4/10

Key Features:

• Powerful data collection infrastructure with proxy rotation

• Structured data retrieval

• Seamless integration with MCP Web Scraper

Use Cases:

• Real-time data collection for LLMs

• Market research and competitor analysis

• Automating RAG pipelines

Pros: Reliable access to any public web data, well-documented, highly scalable

Cons: Paid tiers required for advanced use

2. GitHub MCP

Category: Coding & DevOps

Best For: Coding assistants and repository insights

Official/Community: Official

Price: Free

Setup Difficulty: 3/10

Key Features:

• Interact with repositories, branches, and pull requests

• Access code snippets and metadata directly via MCP

• Secure authentication via GitHub OAuth

Use Cases:

• Code analysis and summaries

• Automated issue ranking and reviews

Pros: Reliable, developer-friendly, officially maintained

Cons: Limited beyond GitHub-specific workflows

3. Postman MCP

Category: API integration

Best For: Developers and API testing automation

Official/Community: Official

Price: Free + paid plans from $19month

Setup Difficulty: 3/10

Key Features:

• API collection management through MCP endpoints

• Multi-environment support for dev and staging

• Agent-level API orchestration

Use Cases:

• Automating API tests and calls

• Building AI agents that connect multiple services

Pros: Mature platform, strong developer ecosystem

Cons: Requires a Postman account and setup

4. Memory Bank / Context Portal

Category: Memory & RAG

Best For: Long-term context storage

Official/Community: Community

Price: Free

Setup Difficulty: 5/10

Key Features:

• Embedding and vector memory storage

• Constant context retrieval across sessions

• Semantic search and caching

Use Cases:

• AI chatbots with long-term memory

• Knowledge-based AI agents

Pros: Critical for RAG workflows, open-source

Cons: Requires manual setup and database setup

5. Rube (Composio)

Category: Connectors & SaaS (Software as a Service)

Best For: Automating multi-app workflows

Official/Community: Community

Price: Free + paid plans from $29/month

Setup Difficulty: 5/10

Key Features:

• 500+ ready-to-use SaaS connectors

• Secure credential management

• Modular workflow creation environment

Use Cases:

• Customer relationship management (CRM) automation

• Report generation

• Data syncing

Pros: Wide integration range for different purposes

Cons: Harder setup for enterprise-level use

6. Playwright MCP

Category: Browser automation

Best For: UI testing and web data extraction

Official/Community: Community (Microsoft-supported project)

Price: Free

Setup Difficulty: 6/10

Key Features:

• Headless browser automation

• Screenshot and DOM parsing

• Cross-browser support (Chromium, Firefox, WebKit)

Use Cases:

• Web testing automation

• Scraping dynamic JavaScript-heavy sites

Pros: Excellent control, highly flexible

Cons: Higher setup complexity

7. Notion MCP

Category: Knowledge base management

Best For: Integrating LLMs with Notion data

Official/Community: Official

Price: Free + paid plans from $8/month

Setup Difficulty: 5/10

Key Features:

• Access to Notion databases and pages

• Real-time data queries

• API-based system for data retrieval

Use Cases:

• AI knowledge assistants

• Research and content planning

Pros: User-friendly, integrated Notion API

Cons: Limited to only Notion environments

8. Sequential Thinking MCP

Category: AI planning

Best For: Reasoning and task planning

Official/Community: Community

Price: Free

Setup Difficulty: 4/10

Key Features:

• Chain-of-thought planning for AI agents

• Multi-step decision logic

Use Cases:

• Building reasoning pipelines

• Task management for AI workflows

Pros: Quality reasoning for AI agents

Cons: Requires a lot of fine-tuning for complex tasks

9. n8n MCP

Category: Workflow automation

Best For: No-code or low-code integrations

Official/Community: Community

Price: Free + paid plans from $20/month (cloud)

Setup Difficulty: 4/10

Key Features:

• Visual builder for automation

• Prebuilt integrations and triggers

• MCP support for AI extensions

Use Cases:

• Automating AI pipelines

• Connecting LLMs to APIs or CRMs

Pros: Easy to use, powerful no-code solution

Cons: Requires cloud or self-hosting setup

10. DB & Semantic Memory MCP

Category: Data & knowledge storage

Best For: RAG systems and context retrieval

Official/Community: Community

Price: Free (Depends on DB host, e.g. Pinecone free tier)

Setup Difficulty: 5/10

Key Features:

• PostgreSQL-based or vector databases

• Continuous context for AI agents

Use Cases:

• RAG pipelines

• Knowledge management for enterprise AI

Pros: Highly beneficial for memory-rich AI apps

Cons: Database configuration required

Best MCP Servers by Use Case

One single tool cannot be a silver bullet for all cases, and MCP servers are no exception. Here we’ll give top recommendations for specific use cases.

Best MCP Servers for Claude Code

Claude Code is a highly popular environment for MCP servers to extend its coding capabilities. The right choice of servers here can help streamline repository access, file management, and web data retrieval, which can be covered by:

• Anthropic Code MCP

• Filesystem MCP Server

• Oxylabs MCP

Anthropic’s own Code MCP is used for smooth communication with Claude’s coding sandbox, while Filesystem MCP enables easy file-level access for reading, writing, and navigation. Oxylabs MCP is a very beneficial addition to an external data layer, allowing Claude Code to fetch real-time structured data and integrate it directly into the coding process.

Best MCP Servers for Cursor

Cursor users usually rely on MCP to provide live code execution, repository insights, and automation within the editor. Some of the highly recommended options are:

• Run Python MCP Server

• GitHub MCP

• Rube (Composio)

Run Python MCP is often used for safer inline code execution, which helps validate and test code on the fly. GitHub MCP is hands-down the best option for full integration with repositories (issues, commits, pull requests) without leaving Cursor. Finally, Rube, with its multi-service connectivity, is a nice addition for Cursor users to link external APIs and services to automate repetitive development tasks, which are quite common in professional environments.

Best MCP Servers for Other AI Coding Tools

Beyond Claude and Cursor, several other AI-assisted IDEs and coding agents can  also benefit from MCP servers that improve context, automation, and data flow, such as:

• Playwright MCP

• Puppeteer MCP Server

• Google Drive MCP Server

These servers extend coding tools with automation and data management capabilities. Playwright and Puppeteer MCPs handle browser interactions and web navigation, ideal for testing or retrieving data from web apps. Meanwhile, Google Drive MCP supports document and file versioning for collaborative development projects.

Best MCP Servers for General Coding & Development

For developers using custom AI environments, self-hosted setups, or LLM customization frameworks, general-purpose MCP servers usually provide that additional flexibility to build advanced automations. Our best pick are:

• Oxylabs MCP

• PulseMCP

• LocalDocs MCP

Oxylabs MCP is best used for unlocking real-time data access and scraping capabilities for integrating live data flows into codebases. PulseMCP acts as a lightweight orchestration layer that connects multiple MCPs for large-scale workflows, while LocalDocs MCP is a very highly regarded tool that helps developers manage private documentation.

Why Use MCP Servers?

MCP servers represent a significant shift in how AI interacts with the real-time world. Traditional APIs or SDKs only act as single communication lines, but MCP standardizes and scales this process for modern AI agents. Using MCP servers can bring loads of benefits, including:

• Speed: AI models access data through efficient pipelines for improved responsiveness.

• Accuracy: Access to real-time data gives more accurate responses.

• Context: Preserve context between sessions.

• Automation: Reduced manual configuration by abstracting complex toolchains.

• Scalability: Easily connect multiple agents and systems with minimal setup.

Developer can build an AI agent that scrapes structured web data, queries SQL databases, and summarizes it into actionable insights – all through interconnected MCP endpoints. For example, connecting an OpenAI agent to the Oxylabs Web Scraper API through an MCP server allows it to gather live market data or monitor product listings automatically.

That said, MCP is not the only protocol used in advanced AI workflows. Agent2Agent (A2A) is a popular protocol still used in various pipelines, so you can compare A2A vs MCP and see which framework fits your AI application and the whole project.

Conclusion

MCP servers are rapidly becoming the foundation of intelligent and context-aware AI applications. Whether you’re running a coding assistant, automating business tasks, or building RAG pipelines, choosing the right MCP server can noticeably improve speed, accuracy, and scalability of your project.

To level up your AI workflows, start with a reliable, production-grade option like Oxylabs MCP, then expand your setup with community servers like Memory Bank, Playwright, or Rube and build highly scalable AI pipelines easier than ever before.