10 Best MCP Servers for Claude (2026 Guide)

When I first connected Claude to MCP servers, it felt like unlocking a different AI entirely. Suddenly, my assistant could read my files, search the web, query databases, and remember things across conversations. The difference was night and day.

But here’s the thing—there are thousands of MCP servers out there now, and the ecosystem is growing rapidly. Finding the ones actually worth installing? That takes some digging. I’ve tested dozens over the past few months, and most are either too niche, poorly maintained, or just not that useful for everyday work.

This guide cuts through the noise. I’ll walk you through the 10 MCP servers that genuinely matter—the ones that transform Claude from a smart chatbot into a capable assistant that can actually do things. For each one, I’ll explain what it does, why you’d want it, and how to get started. I’ve also included troubleshooting tips, security best practices, and advanced workflows that combine multiple servers for maximum productivity.

Let’s expand what Claude can do.

What Are MCP Servers? Quick Refresher

If you’re new to MCP, here’s the short version: The Model Context Protocol (MCP) is a standard developed by Anthropic that lets AI applications connect to external tools and data sources. An MCP server is a program that exposes specific capabilities—reading files, accessing APIs, querying databases—through this standard protocol. Think of it as a universal adapter that lets Claude plug into any system.

When you connect an MCP server to Claude Desktop, Claude gains new abilities. It can call the server’s tools, read its resources, and use its prompts. The AI doesn’t need custom code for each integration—it just speaks MCP. This is fundamentally different from custom integrations because MCP servers work across different AI platforms, not just Claude.

For a deeper dive, check out our complete guide on what MCP is and how it works. Or if you want to build your own, we have a Python MCP server tutorial.

Now, let’s look at the servers worth installing.

How to Install MCP Servers

Before we dive into specific servers, here’s the general installation process for Claude Desktop:

Find the server - Most are on GitHub or the official MCP server directory
Install dependencies - Usually via npm or pip, depending on the server
Configure Claude Desktop - Add the server to your config file:
- Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
- Windows: %APPDATA%\Claude\claude_desktop_config.json
Restart Claude Desktop - New servers appear after restart

A typical config entry looks like this:

{
  "mcpServers": {
    "server-name": {
      "command": "node",
      "args": ["/path/to/server/index.js"]
    }
  }
}

Each server has specific installation instructions—I’ll note the key steps for each one below.

The 10 Best MCP Servers

1. Filesystem Server

What it does: Gives Claude read and write access to files on your computer.

Why you want it: This is the foundational MCP server. Without it, Claude can’t interact with your local files—it can only work with what you paste into the chat. With it, you can say “summarize the PDF on my desktop” or “read my config file and explain it” and Claude actually does it.

Key features:

Read files and directories
Write and create files
Search within files
Configurable access controls (you can limit which folders Claude sees)
Support for common text formats (markdown, JSON, YAML, code files, and more)

What makes it essential: Almost every useful workflow involves files. Whether you’re coding, writing, analyzing data, or organizing documents, file access is table stakes. I’ve found this particularly valuable for:

Code reviews: “Read all the Python files in src/ and identify potential security issues”
Documentation: “Create a README based on the code structure in this project”
Analysis: “Summarize the main points from all the markdown files in my notes folder”
Refactoring: “Update all JavaScript files to use ES6 arrow functions”

Setup tip: Be thoughtful about which directories you allow. The principle of least privilege applies—only grant access to folders Claude actually needs. Start with a specific project folder before granting broader access.

Common use patterns:

Task	Example Prompt
Read code	”Read main.py and explain what it does”
Search files	”Find all files mentioning ‘authentication‘“
Batch operations	”Rename all .txt files to .md in docs/“
Cross-file analysis	”Compare package.json across my three projects”

For more on working with files effectively, see our guide on Claude Desktop MCP setup which covers file access patterns in depth.

{
  "filesystem": {
    "command": "npx",
    "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/you/Documents"]
  }
}

2. Git Server

What it does: Lets Claude interact with Git repositories—reading history, viewing diffs, understanding code changes.

Why you want it: If you work with code, this is invaluable. Claude can review your recent commits, explain what changed, help with merge conflicts, and understand the evolution of your codebase over time.

Key features:

View commit history
Show diffs between versions
Read file contents at specific commits
Search repository history
Branch and tag information

Real use case: I’ve used this to ask “what changes were made to the authentication module this week?” and get a coherent summary of commits, changes, and patterns—far faster than digging through git log myself.

Setup tip: Point it at your most active projects. The server works with any local Git repository.

3. GitHub Server

What it does: Connects Claude to GitHub’s API for repository management, issues, pull requests, and more.

Why you want it: This extends beyond local Git. Claude can read your GitHub issues, review pull requests, check workflow status, and even create issues or comments. It’s like having an AI that understands your entire development workflow.

Key features:

Read and create issues
Review pull requests
Check CI/CD status
View repository contents
Manage labels and milestones

Real use case: “Show me all open bugs in the backend repo and suggest which ones I should tackle first based on complexity.” Claude pulls the issues, analyzes them, and gives prioritized recommendations.

Setup tip: Requires a GitHub personal access token. Create one with appropriate scopes (repo access, issues, PRs) and configure it securely.

4. Fetch/Web Server

What it does: Allows Claude to fetch and read web pages, converting HTML to markdown for processing.

Why you want it: Claude’s training data has a cutoff. When you need current information from the web—documentation, articles, reference material—this server lets Claude go get it directly.

Key features:

Fetch any public URL
Convert HTML to markdown
Handle various content types
Respect robots.txt (configurable)

Real use case: “Read the latest Next.js documentation on server components and summarize the key changes.” Claude fetches the page, reads it, and gives you a summary—far better than trying to describe a documentation page yourself.

Limitation: This is read-only web access. It can’t interact with pages that require login or handle dynamic JavaScript content.

5. Memory Server

What it does: Provides persistent memory using a knowledge graph, allowing Claude to remember information across conversations.

Why you want it: Default Claude forgets everything when you start a new conversation. This server changes that. You can tell Claude to remember facts, preferences, and context, and it actually retains them.

Key features:

Store key-value pairs
Create relationships between concepts
Query stored knowledge
Persistent across sessions

Real use case: “Remember that I prefer TypeScript over JavaScript for new projects.” Next conversation: “Should I use TypeScript for this new project?” Claude remembers your preference and factors it in.

This is huge for: Building a truly personalized AI assistant that learns your preferences, remembers your projects, and accumulates context over time.

Setup tip: The memory persists to a local file. Consider what you want Claude to remember—and what you don’t.

6. PostgreSQL Server

What it does: Connects Claude to PostgreSQL databases, allowing it to run queries and analyze data.

Why you want it: If you work with data, this is transformative. Instead of writing SQL, you describe what you want in plain English. “Show me users who signed up last month but haven’t made a purchase.” Claude writes and executes the query.

Key features:

Run SELECT queries
Explore database schema
Describe tables and relationships
Safe read-only mode available

Real use case: Data analysis becomes conversational. “What’s the average order value by customer segment?” “Which products are most often bought together?” Claude handles the SQL—you focus on the questions.

Security note: Consider using a read-only database user. You probably don’t want your AI accidentally deleting data, even if you trust it.

7. Brave Search Server

What it does: Gives Claude web search capabilities through Brave’s privacy-focused search API.

Why you want it: When Claude needs current information, this lets it search the web rather than relying on potentially outdated training data. Unlike the Fetch server, this searches—it doesn’t just fetch known URLs.

Key features:

Web search with privacy focus
News search
Local search (for location-based queries)
Returns structured results

Real use case: “What are the latest developments in quantum computing this month?” Claude searches, synthesizes results, and gives you a summary with sources.

Setup tip: Requires a Brave Search API key (free tier available). The API is rate-limited, so heavy usage may need a paid plan.

8. Slack Server

What it does: Connects Claude to your Slack workspace for reading messages and sending notifications.

Why you want it: This bridges Claude into your team communication. It can summarize channel activity, search for specific discussions, and even post messages on your behalf.

Key features:

Read channel messages
Search message history
Send messages to channels
Access user information

Real use case: “Summarize what the engineering team discussed in #backend today.” Claude reads the channel, identifies key topics, and gives you a digest without you scrolling through hundreds of messages.

Security consideration: Slack integrations have access to your messages. Make sure you understand the permissions you’re granting.

9. Notion Server

What it does: Connects Claude to your Notion workspace for reading and managing pages, databases, and notes.

Why you want it: Notion is where many teams keep documentation, notes, project tracking, and knowledge bases. With this server, Claude becomes an intelligent interface to all that information.

Key features:

Read pages and databases
Search content
Create and update pages
Query database entries

Real use case: “Find everything we’ve documented about our API authentication.” Claude searches your Notion, compiles relevant information, and presents it coherently—even if it’s scattered across multiple pages.

Power combo: Combine with the Memory server, and Claude can remember which Notion pages are most relevant to your work.

10. Sequential Thinking Server

What it does: Enables Claude to engage in structured, step-by-step reasoning with reflection and revision capabilities.

Why you want it: This isn’t about connecting to external data—it’s about enhancing how Claude thinks. The server provides a framework for breaking down complex problems, recording reasoning steps, and revising conclusions.

Key features:

Step-by-step thought management
Hypothesis tracking
Revision capabilities
Confidence scoring

Real use case: For complex problems—designing a system architecture, debugging a tricky issue, planning a project—this helps Claude think more systematically. It’s like giving the AI a scratchpad for working through difficult questions.

When to use it: Any task that benefits from structured thinking rather than quick responses. Research, analysis, planning, and problem-solving all improve.

Detailed Setup Walkthroughs

Let me walk you through setting up the most essential servers step by step.

Setting Up the Filesystem Server

The filesystem server is foundational. Here’s exactly how to set it up:

Step 1: Install Node.js (if not already installed)

Download from nodejs.org or use your package manager.

Step 2: Configure claude_desktop_config.json

Find your config file:

Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

Add this configuration:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/Users/yourname/Documents",
        "/Users/yourname/Projects"
      ]
    }
  }
}

Step 3: Restart Claude Desktop

The server loads on startup. After restarting, you should see a hammer icon indicating tools are available.

Step 4: Test it

Ask Claude: “List the files in my Documents folder” or “Read the README in my Projects directory.”

Pro tip: Add only directories you want Claude to access. The principle of least privilege applies here.

Setting Up the Memory Server

Persistent memory transforms Claude from a forgetful assistant to a personalized partner:

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-memory"]
    }
  }
}

How to use it:

Tell Claude “Remember that I prefer TypeScript for new projects”
Ask Claude facts later: “What language do I prefer for new projects?”
View stored memories: “What do you remember about my preferences?”

The memory persists to a local SQLite database. You can review it or clear it if needed.

Setting Up PostgreSQL Server

For data access, the PostgreSQL server requires a database connection string:

{
  "mcpServers": {
    "postgres": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-postgres",
        "postgresql://user:password@localhost:5432/database"
      ]
    }
  }
}

Important: Use a read-only database user for safety:

CREATE USER claude_readonly WITH PASSWORD 'secure_password';
GRANT CONNECT ON DATABASE yourdb TO claude_readonly;
GRANT SELECT ON ALL TABLES IN SCHEMA public TO claude_readonly;

This lets Claude query your data without any risk of accidental modifications.

Troubleshooting Common Issues

MCP is relatively new, and you’ll likely hit some bumps. Here’s how to fix the most common problems:

Server Not Loading

Symptoms: No hammer icon, Claude says it can’t access tools.

Fixes:

Check JSON syntax — A trailing comma or missing bracket breaks the whole config
Verify paths — All paths must be absolute, not relative
Check permissions — Claude Desktop needs read access to directories you’re exposing
Restart fully — Quit Claude Desktop completely (not just close the window), then reopen

Use this to validate your JSON:

cat ~/Library/Application\ Support/Claude/claude_desktop_config.json | jq .

If jq reports an error, fix the syntax before trying again.

Server Crashes on Start

Symptoms: Server appears briefly then disappears.

Fixes:

Check Node.js version — Most servers need Node 18+
Update npm packages — Run npx -y @modelcontextprotocol/server-<name> to get latest
Check for port conflicts — Some servers use specific ports
Review logs — Check Console.app (Mac) or Event Viewer (Windows) for error messages

Slow Response Times

Symptoms: Claude takes forever to respond when using tools.

Fixes:

Reduce scope — Limit directories to only what’s needed
Add .gitignore-style patterns to exclude large folders like node_modules
Check network — For servers that fetch external data, network latency matters
Consider server resources — Memory server with millions of entries will slow down

Permission Denied Errors

Symptoms: “Access denied” or “Permission error” messages.

Fixes:

Check filesystem permissions — Ensure your user can read/write the directories
Verify API keys — For GitHub, Brave Search, etc., expired keys cause permission errors
Review OAuth tokens — Some enterprise integrations need token refresh

Security Best Practices

MCP servers can access sensitive data. Take security seriously:

Principle of Least Privilege

Only grant access to what Claude actually needs:

// ❌ Too broad - gives access to everything
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/"]

// ✅ Specific - only project directories
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/me/Projects"]

Database Security

For PostgreSQL and other database servers:

Always use read-only users unless write access is truly necessary
Limit schemas — Grant access only to specific schemas, not the entire database
Avoid production — Use staging databases or read replicas, never primary production
Audit logs — Enable query logging to track what Claude accesses

API Key Management

For servers requiring API keys (GitHub, Brave Search, etc.):

Use environment variables rather than hardcoding in config
Rotate keys regularly — Set calendar reminders
Limit scopes — GitHub tokens should only have necessary permissions
Review access — Periodically audit what integrations have access to what

Data Privacy Considerations

Don’t expose PII through filesystem or database servers unless necessary
Consider local-only servers for sensitive data rather than cloud integrations
Review what Memory stores — It can accumulate personal information over time
Check enterprise policies — Some organizations have rules about AI data access

For teams building their own MCP servers, our guide on building custom MCP servers in Python covers security patterns in detail.

Advanced Workflows and Combinations

The real power of MCP emerges when you combine servers for sophisticated workflows.

Developer Productivity Stack

Combine Filesystem + Git + GitHub + Memory for a complete development assistant:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/me/code"]
    },
    "git": {
      "command": "npx", 
      "args": ["-y", "@modelcontextprotocol/server-git", "--repository", "/Users/me/code/myproject"]
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_TOKEN": "ghp_xxxx"
      }
    },
    "memory": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-memory"]
    }
  }
}

What you can do:

“Review my recent commits and suggest improvements”
“Look at the open issues and recommend which I should tackle first”
“Remember that this project uses PostgreSQL, not MySQL”
“Read the auth module and suggest security improvements”

Research and Analysis Stack

Combine Brave Search + Fetch + Sequential Thinking + Memory:

What you can do:

“Research the current state of AI image generation and give me a structured analysis”
“Find the latest papers on RAG optimization and summarize the key findings”
“Remember the key insights for my report on market trends”

Data Analysis Stack

Combine PostgreSQL + Filesystem + Memory:

What you can do:

“Analyze customer churn by cohort and export the results to a CSV”
“Compare this quarter’s metrics to last quarter and create a summary document”
“Remember that we define active users as those with login in past 30 days”

Team Communication Stack

Combine Slack + Notion + Memory:

What you can do:

“Summarize what the engineering team discussed today and update our Notion project page”
“Find all mentions of the API redesign and compile the key decisions made”
“Remember that the launch date moved to Q2”

For more on designing AI workflows, see our guide on agentic AI patterns and architectures.

Performance Optimization Tips

Once you have multiple servers running, optimization matters:

Startup Time

Each server adds startup latency. To minimize:

Only install servers you actively use
Prefer npm servers (faster startup) over Python when alternatives exist
Use local servers rather than remote APIs when possible

Memory Usage

Monitor memory consumption, especially with:

Memory server — Large knowledge graphs consume RAM
Filesystem server — Processing large directories uses memory during indexing
Database servers — Connection pooling matters for multiple queries

Response Latency

For faster responses:

Pre-warm connections — Database connections take time to establish
Limit search scope — Broad filesystem searches are slow
Use caching — Memory server helps avoid repeated lookups
Consider local models — For sensitive data, running Claude locally eliminates network latency

Enterprise MCP Integrations

Beyond the core servers above, several enterprise integrations are now available through Claude’s official partnerships:

Atlassian (Jira & Confluence)

Claude can create and manage Jira tickets, search Confluence documentation, and bridge project management with AI assistance. Great for development teams already in the Atlassian ecosystem.

Zapier

This integration is wild—it connects Claude to thousands of apps through Zapier’s automation platform. Trigger workflows, query data across services, and build complex automations with natural language.

Google Workspace

Gmail, Google Drive, Calendar—Claude can search your emails, find documents, and understand your schedule. Particularly useful for professional productivity.

Payment Platforms (Stripe, Square, PayPal)

For business applications, these integrations let Claude help with invoice management, payment status, and financial data analysis.

Setting these up typically requires:

A Claude Pro or Team subscription
Authentication with the third-party service
Configuration through Claude’s settings

For the official documentation on available integrations, check Anthropic’s integration hub.

Building Your MCP Server Stack

Here’s how I’d recommend approaching MCP servers:

Start essential: Install Filesystem and Memory first. These are foundational—file access and persistent memory transform the basics of what Claude can do.

Add based on workflow: A developer? Add Git and GitHub. Working with data? PostgreSQL. Need current information? Brave Search or Fetch.

Don’t overcollect: More servers mean more to maintain and more potential security surface. Install what you’ll actually use.

Consider combinations: Some servers are powerful together. Memory + Filesystem lets Claude remember project contexts across sessions. PostgreSQL + Slack could summarize data then share insights with your team.

The complete list of servers is growing constantly. The official MCP directory and GitHub are the best places to discover new ones. For a comprehensive reference, our MCP server directory guide catalogs the most useful options.

Frequently Asked Questions

Are MCP servers free?

Most are open source and completely free. Some enterprise integrations (like Zapier) may have usage-based costs on the third-party side.

Can I use MCP servers with ChatGPT or other AI?

Yes—MCP is an open standard now supported by OpenAI, Google, Microsoft, and others. Servers built to the MCP spec work across compatible clients. The protocol is designed for interoperability.

Are MCP servers safe?

Servers from official sources and well-maintained projects are generally safe. Be cautious with random third-party servers—they can access whatever you configure them to access. Review permissions carefully and follow the security best practices outlined above.

Can I run multiple MCP servers at once?

Absolutely. Claude handles multiple connected servers seamlessly. The config file supports as many servers as you need. I routinely run 4-5 servers simultaneously without issues.

How do I update MCP servers?

Most servers installed via npm or pip can be updated with their respective package managers. For npm servers, just delete the cached version and let it re-download:

rm -rf ~/.npm/_npx/@modelcontextprotocol

Next time Claude starts, it’ll download fresh versions.

Can I build my own MCP server?

Absolutely! MCP is an open protocol, and building custom servers is straightforward. We have a complete tutorial on building MCP servers in Python that walks through the process step by step.

What’s the difference between MCP and function calling?

Function calling is built into specific AI models. MCP is a protocol that works across models and tools. They can work together—MCP provides the interface, function calling provides the mechanism. See our detailed comparison of MCP vs function calling for more.

Do MCP servers work offline?

Most core servers (Filesystem, Git, Memory, PostgreSQL with local database) work fully offline. Servers that fetch external data (Brave Search, Fetch, GitHub) obviously require internet connectivity.

Supercharging Claude

MCP servers represent a fundamental shift in what AI assistants can do. They’re not just answering questions—they’re taking actions, accessing data, and integrating into your actual workflows.

The 10 servers in this guide cover the essentials: file access, code management, search, data, memory, communication, and enhanced reasoning. Start with what you need most, experiment with combinations, and build from there.

The best part? This is just the beginning. The MCP ecosystem is growing rapidly, with new servers appearing weekly. The AI assistant of tomorrow will be even more capable—and it’ll speak MCP.

Ready to get started? Pick two servers from this list that match your work, install them, and see what Claude can do. You might be surprised at the difference.

Want to dive deeper into MCP? Check out our complete guide on what MCP is and how it works or explore our MCP resources, tools, and prompts collection.