PulseMCP now lists over 9,000 MCP servers. Six months ago that number sat closer to 1,000. The ecosystem grew ninefold and the signal-to-noise ratio dropped with it.
This guide cuts through the noise. We tested dozens of MCP servers across eight categories and ranked the ones worth installing in your OpenClaw or Claude Desktop setup. Every pick below is based on real usage data from the FastMCP registry (views, installs), GitHub activity, and hands-on testing.
Whether you need a database connector, a browser automation layer, or a way to search the web from inside your agent, there is a section here for you.
1. Developer tools and code
Code-related servers account for the largest slice of the MCP ecosystem. Most developers start here, and for good reason. These servers turn your AI agent into a proper development partner that can read repos, open PRs, and run code.
GitHub MCP
The official GitHub MCP server is the one to install first if you write code for a living. It is written in Go, maintained by GitHub themselves, and covers the full API surface: repo management, pull requests, issues, code search, and file operations.
On FastMCP it shows 3,100 views and 204 installs. Those numbers are modest compared to some community servers, but the reliability gap matters. Official maintenance means it stays current with GitHub API changes.
Here is a minimal OpenClaw config to get it running:
{
"mcpServers": {
"github": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"],
"env": {
"GITHUB_PERSONAL_ACCESS_TOKEN": "your-token-here"
}
}
}
}For a full walkthrough on config files and transports, see our guide to connecting MCP servers.
Context7
Context7 is the #1 most popular MCP server on FastMCP with 11,000 views and 690 installs. It solves a specific pain point: AI models trained on old documentation generate outdated code.
Context7 injects version-specific docs for 50+ frameworks directly into your AI's context window. When you ask Claude to write a Next.js 15 component, Context7 feeds it the actual Next.js 15 docs instead of whatever version the model memorized during training.
If you spend time correcting your AI's outdated syntax suggestions, this server pays for itself on day one.
Supabase MCP
The Supabase MCP server gives your agent access to your entire Supabase backend: database queries, auth management, storage buckets, and edge functions. One server replaces what would otherwise require four or five separate connections.
Best suited for teams already on Supabase who want their AI to manage migrations, inspect tables, or debug auth flows without leaving the conversation.
Desktop Commander
Desktop Commander handles terminal access, process management, and ripgrep-powered file search. It sits at roughly 2,800 views on FastMCP and is the go-to for local file operations that go beyond what the basic filesystem server offers.
Think of it as giving your agent a proper terminal session. It can run shell commands, manage background processes, and search through codebases using ripgrep patterns.
E2B
When you need to execute untrusted code safely, E2B is the answer. It runs Python and JavaScript in sandboxed cloud environments. Each execution gets its own isolated container, so a bad script cannot touch your local machine.
Ideal for code review workflows where you want to test generated code before committing it.
Pro tip: Pair the GitHub MCP server with Context7 for the best developer experience. GitHub handles the repo operations while Context7 keeps the AI current on framework APIs. Together they cover reading, writing, and referencing code.
2. Web search and research
AI models have a knowledge cutoff. Web search servers close that gap by letting your agent pull live results mid-conversation. Four servers dominate this category, each optimized for different use cases.
Brave Search MCP
Privacy-focused general web search at $5 per 1,000 queries. Brave does not track users or personalize results, which makes it predictable for automated pipelines. The results are broad and well-suited for general browsing tasks where you need a quick answer from the open web.
Here is the OpenClaw config:
{
"mcpServers": {
"brave-search": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-brave-search"],
"env": {
"BRAVE_API_KEY": "your-api-key-here"
}
}
}
}Tavily MCP
Tavily was built specifically for AI agents. Its results come pre-formatted with citation-ready snippets, which means less post-processing for your agent. The free tier gives you 1,000 searches per month, which is enough for personal use or testing.
For research-heavy workflows where you need sourced claims, Tavily consistently outperforms general search APIs.
Exa MCP
Exa takes a different approach. Instead of keyword matching, it uses semantic and similarity search. You describe what you are looking for in natural language and Exa finds conceptually similar content. This makes it excellent for research tasks where you know the idea but not the exact terms.
Perplexity MCP
Perplexity combines search with AI-generated summaries. Each result includes sourced citations, making it the most conversational option. Best for tasks where you want a synthesized answer rather than a list of links.
Quick comparison: Use Brave for general browsing. Pick Tavily for citation-heavy research. Choose Exa for semantic similarity when keywords fail. Go with Perplexity for conversational answers with sources.
3. Web scraping and browser automation
Three servers own this category, and one of them holds the #2 spot across the entire MCP ecosystem.
Playwright MCP (Microsoft)
Microsoft's Playwright MCP server sits at 5,600 views and 414 installs, making it the #2 most popular server on FastMCP overall. What makes it special is the approach: instead of taking screenshots and feeding pixels to the AI, it uses accessibility snapshots. The AI reads the page structure directly as a tree of elements, roles, and text content.
This means faster execution, lower token usage, and more accurate interactions compared to vision-based approaches. Playwright alone accounts for three of the top ten most popular MCP servers on FastMCP (the core server plus framework-specific variants).
Firecrawl MCP
Firecrawl has crossed 85,000 GitHub stars, making it one of the most popular open-source scraping tools period. The MCP server converts any URL into clean markdown, stripping ads, navigation chrome, and cookie banners. Feed it a documentation page and you get back pure content your AI can work with.
Pair it with Playwright for a complete web interaction stack: Firecrawl reads pages, Playwright drives browsers.
Puppeteer MCP
At 4,200 views and 199 installs, Puppeteer MCP takes the Chrome DevTools Protocol approach. It excels at screenshot generation, PDF creation, and scenarios where you need pixel-level control. If your workflow involves generating visual reports or capturing page states, Puppeteer handles that better than Playwright's accessibility-first model.
Pro tip: If you are building a custom scraping pipeline, check our guide to building MCP servers in Python. Wrapping an existing scraper as an MCP server takes about 30 minutes with FastMCP.
4. Databases
Connecting your AI to a database is powerful and risky. The servers below range from read-only query tools to full optimization suites. Pick based on how much access you are comfortable granting.
PostgreSQL MCP (official)
Anthropic's official PostgreSQL server is deliberately limited to read-only queries and schema inspection. That constraint is the feature. You can point it at a production database without worrying about accidental writes or deletions. Schema inspection lets the AI understand your data model before writing queries, which improves query accuracy.
Postgres MCP Pro
Postgres MCP Pro by CrystalDBA goes further. It adds health analysis, index tuning recommendations, and query optimization. Point it at a slow database and ask your AI to explain why a query takes 30 seconds. It will inspect execution plans, suggest missing indexes, and explain the bottleneck.
This server is for database administrators and backend engineers who want AI-assisted performance work, not just data retrieval.
SQLite MCP
For lightweight local databases, the SQLite server covers schema inspection, CRUD operations, and ad-hoc queries. Good for prototyping, local data analysis, and projects that do not need a full database engine.
MindsDB MCP
MindsDB takes a different angle entirely. It supports federated queries across multiple databases and SaaS tools. Connect your Postgres, MySQL, and Salesforce data, then query across all three in a single SQL statement. Useful for teams with data spread across many systems.
Pro tip: Always use read-only mode for production databases. Write access through MCP should be limited to development and staging environments. One misinterpreted prompt can drop a table.
5. Productivity and business tools
This is where MCP servers start replacing manual workflows. Instead of switching between apps, your AI agent handles updates across Google Workspace, Notion, Slack, and Stripe from a single conversation.
Google Workspace MCP
One server covers the full Google stack: Gmail, Docs, Sheets, Drive, Calendar, and Tasks. Authentication uses OAuth, so you grant access once and it persists. The breadth here is the selling point. Instead of installing six separate servers, you get the entire suite through a single connection.
Notion MCP
Full CRUD access to Notion: create and edit pages, query databases, manage comments, and search across your workspace. Particularly useful for teams that use Notion as their knowledge base. Ask your AI to find a spec, update a status, or summarize a database view.
Slack MCP
Channel search, message posting, and thread management. The Slack server turns your AI into a team communication tool. It can search for past discussions, post updates, and follow up in threads. Works best when paired with other productivity servers so your AI can, say, check a Notion task and post a Slack update about it.
Stripe MCP (official)
Stripe's official server exposes payments, customers, subscriptions, and invoices. Built by Stripe's own team, it follows their API conventions closely. Useful for support workflows where you need to look up a customer's payment history or check subscription status mid-conversation.
These productivity servers are where OpenClaw's messaging-first interface stands out. You can trigger Notion updates or Slack messages from WhatsApp, turning your phone into a control center for your entire tool stack.
6. Design and frontend
Design-to-code workflows have gotten better with MCP. These servers bridge the gap between what designers create and what developers implement.
Figma MCP (official Dev Mode)
The official Figma server reads live design structure, auto-layout properties, variants, and text styles. It does not just export images. It gives your AI the actual design hierarchy, so generated code matches the design's structure, not just its appearance.
You need Figma Dev Mode access (paid plan) to use it. Once connected, your AI can generate React components that match your design system's spacing, colors, and typography without manual specification.
ReactBits
Sitting at 4,800 views and 207 installs (#4 on FastMCP), ReactBits specializes in React and Tailwind component generation. It maintains a library of component patterns and generates production-ready code with proper responsive behavior and accessibility attributes.
Magic UI MCP
Magic UI focuses on animated React components. If your project needs motion design, loading animations, or interactive transitions, this server provides a library of pre-built animation patterns your AI can reference and customize.
7. Infrastructure and monitoring
Ops teams are starting to use MCP servers for incident response and infrastructure management. The tooling is newer here, but a few servers already deliver real value.
Grafana MCP
The Grafana MCP server connects your AI to incident management, Prometheus and Loki queries, and alerting rules. During an incident, you can ask your AI to pull recent error rates, check which alerts fired, and correlate logs with metrics. That beats clicking through dashboards when you are under pressure.
Sentry MCP
Sentry's MCP server is still in beta, but it already supports protocol-aware monitoring for MCP server health. It tracks error rates, latency, and throughput for your MCP connections. Useful if you run multiple servers and need visibility into which ones are dropping requests.
AWS MCP suite
Amazon released official MCP servers for DynamoDB, Aurora, and Neptune. Each server follows the same configuration pattern, making it straightforward to connect multiple AWS data stores. If your infrastructure runs on AWS, these official servers are more reliable than community alternatives.
Docker MCP Toolkit
Not a single server, but a platform for running them. The Docker MCP Toolkit offers 300+ containerized servers with one-click installation. Each server runs in its own container, limited to 1 CPU and 2 GB of RAM. That isolation means a misbehaving server cannot take down your machine.
The Docker MCP Catalog is also one of the best places to discover new servers, since every listing is verified and containerized.
8. How to find more servers
Eight categories cannot cover every use case. Here is where to look when you need something specific:
- PulseMCP directory (9,080+ servers): the largest searchable index of MCP servers. Filter by category, transport type, and popularity.
- Docker MCP Catalog (300+ verified): every server is containerized and tested. Best for production use.
- awesome-mcp-servers (GitHub): community-curated list with brief descriptions. Good starting point for exploration.
- OpenClaw's mcp-hub skill (1,200+ servers): search and install servers from inside the agent itself.
- FastMCP registry: includes usage rankings (views, installs) so you can sort by popularity.
- MCP Market: daily leaderboards showing trending servers and new releases.
9. Security before you install
Every MCP server you install gets access to whatever tools and data you grant it. That power requires caution.
Run third-party servers in Docker containers. The Docker MCP Toolkit makes this easy, but even a manual docker run command adds real isolation. A containerized server cannot access your filesystem, environment variables, or other processes unless you explicitly mount them.
Audit source code before granting data access. Popular does not mean safe. Check the server's GitHub repo for recent commits, open issues, and contributor history. A server with one anonymous commit and no issues deserves extra scrutiny.
Use read-only modes where available. Database servers, filesystem servers, and API connectors often offer read-only flags. Start there and upgrade to write access only when your workflow requires it.
For a deeper look at MCP security practices, read our guides on OpenClaw MCP security and Docker sandboxing for MCP servers.
Wrapping up
The MCP ecosystem crossed 9,000 servers and shows no sign of slowing down. That growth also makes choosing harder. The picks in this guide represent the most reliable, most actively maintained, and most widely used servers across each category.
Start with one category that matches your current workflow. Install one or two servers, test them for a week, and add more as you find gaps. That incremental approach beats installing twenty servers on day one and drowning in tool noise.
If you need help connecting your first server, our step-by-step connection guide covers Claude Desktop, Claude Code, and OpenClaw. And if none of the existing servers do what you need, our Python MCP server tutorial walks you through building one from scratch in about an hour.
Which MCP server will you try first?