Top 10 MCP Servers You Should Know About in 2026

The MCP ecosystem is growing rapidly, with new servers being released every week. Here's our curated list of the top 10 MCP servers that every AI power user should know about.

New to MCP? Start with our introduction to the Model Context Protocol. Ready to build your own server? Follow our step-by-step tutorial.

1. Filesystem Server

What it does: Provides secure file operations with configurable access controls.

Why it's essential: The filesystem server is often the first MCP server people install. It allows AI applications to read, write, and manage files on your local machine — essential for any coding assistant.

Key Features:

Read and write files

List directory contents

Create and delete files

Configurable allowed directories for security

Use Cases:

Code editing and generation

Document processing

Configuration management

Link: GitHub - Filesystem Server

2. GitHub Server

What it does: Full GitHub integration including repository management, issues, and pull requests.

Why it matters: If you work with code, you work with GitHub. This server lets AI assistants manage your entire GitHub workflow.

Key Features:

Create and manage repositories

Read and update issues

Work with pull requests

Access file contents from repos

Use Cases:

Automated issue triage

Code review assistance

Repository management

3. Slack Server

What it does: Channel management and messaging capabilities for Slack workspaces.

Why it's useful: Slack is where modern teams communicate. This server enables AI-powered Slack automation.

Key Features:

Send and read messages

Manage channels

Search message history

React to messages

Use Cases:

Automated notifications

Meeting summaries to channels

Team communication bots

4. PostgreSQL Server

What it does: Read-only database access with schema inspection.

Why it's valuable: Database access is crucial for data-driven AI applications. Query your data directly from your AI assistant.

Key Features:

Execute read-only SQL queries

Inspect database schema

List tables and relationships

Safe, read-only access by default

Use Cases:

Data analysis

Report generation

Schema documentation

5. Google Drive Server

What it does: File access and search capabilities for Google Drive.

Why you need it: If your documents live in Google Drive, this server brings them to your AI assistant.

Key Features:

Search files and folders

Read document contents

Access shared drives

List recent files

Use Cases:

Document search and retrieval

Content analysis across files

Automated documentation

6. Brave Search Server

What it does: Web and local search using Brave's Search API.

Why it's great: Give your AI the ability to search the web for current information.

Key Features:

Web search with results

Local business search

News search

Image search

Use Cases:

Research assistance

Fact-checking

Current events context

7. Memory Server

What it does: Knowledge graph-based persistent memory system.

Why it's unique: This server gives AI applications persistent memory across sessions — remember conversations, preferences, and context.

Key Features:

Create and query knowledge graphs

Store entities and relationships

Persist information across sessions

Semantic search capabilities

Use Cases:

Personal AI assistants

Long-term context retention

Relationship tracking

8. Puppeteer Server

What it does: Browser automation and web scraping capabilities.

Why it's powerful: When you need AI to interact with web pages, Puppeteer server makes it possible.

Key Features:

Navigate to URLs

Click and type on pages

Take screenshots

Extract page content

Use Cases:

Web scraping

Automated testing

Form filling

Screenshot capture

9. Sequential Thinking Server

What it does: Dynamic and reflective problem-solving through thought sequences.

Why it's interesting: This server helps AI break down complex problems into manageable steps.

Key Features:

Step-by-step reasoning

Thought chain management

Problem decomposition

Reflective analysis

Use Cases:

Complex problem solving

Decision analysis

Planning and strategy

10. Fetch Server

What it does: Web content fetching and conversion for efficient LLM usage.

Why it's practical: Fetch and process web content in a format optimized for AI consumption.

Key Features:

Fetch URL contents

Convert to readable format

Handle various content types

Rate limiting built-in

Use Cases:

Research assistance

Content aggregation

Documentation fetching

Honorable Mentions

Notion Server: For those who live in Notion, this server brings your workspace to AI.

Sentry Server: Retrieve and analyze issues from Sentry for debugging assistance.

Time Server: Time and timezone conversion — simple but surprisingly useful.

Git Server: Read, search, and manipulate Git repositories directly.

How to Install MCP Servers

Most MCP servers can be installed via npm:

npm install -g @modelcontextprotocol/server-filesystem

Then add to your Claude Desktop config:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/allowed/dir"]
    }
  }
}

Building Your MCP Stack

The servers you choose depend on your workflow:

For Developers:

Filesystem + GitHub + Git + Puppeteer

For Data Analysts:

PostgreSQL + Google Drive + Fetch + Brave Search

For Team Leads:

Slack + GitHub + Memory + Notion

For Researchers:

Brave Search + Fetch + Memory + Sequential Thinking

The Future of MCP Servers

The ecosystem is expanding rapidly. We're seeing:

More official integrations from major platforms

Specialized servers for niche use cases

Better security and permission models

Remote server hosting options

Keep an eye on the MCP Registry for newly published servers.

Conclusion

These 10 servers represent the core of what MCP can do today. Start with a few that match your workflow, then expand as you discover new needs. The modular nature of MCP means you can build exactly the AI toolkit you need.