There are more MCP servers coming out every day, and the list is getting long. There are a lot of good MCP servers, and a lot that might be useless to you. In this article, we go through what we think are the best MCP servers for developers right now. Some are for coding directly, some are for the infrastructure around code (databases, deployments, monitoring), and a few are just genuinely useful for a developer’s day-to-day. If you haven’t tried connecting an AI assistant to one of these yet, do it now. The results feel far more reliable than plain chat.
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What Are MCP Servers, and Why They Matter for Developers
The Model Context Protocol (MCP) is an open standard that lets AI agents and AI assistants talk to external systems through a consistent, standardized interface, instead of every tool needing its own custom integration. MCP servers are what implement that standard on the other side: each one exposes a set of tools that an AI client (Claude Desktop, Claude Code, Cursor, or a custom agent) can call directly.
That solves a real problem. Without MCP, connecting an AI model to your GitHub repositories, your database, or your deployment pipeline means writing bespoke glue code for every single integration. MCP servers solve this by giving the model a standardized interface it already knows how to use: schema discovery, tool calls, and structured responses, the same shape regardless of which system is on the other end.
The practical effect for developers: MCP servers reduce integration time from weeks to days, since the AI client already knows how to talk to any MCP-compliant server. They automate a lot of the validation that used to require manual code, and they meaningfully improve onboarding speed, since a new developer connects to an existing MCP server instead of learning a new SDK for every internal tool the team uses.
How We Picked These
We didn’t just list every MCP server that exists. Our evaluation focused on four things:
Real usefulness for a working developer. Not a tech demo, something that fits into an actual development process.
Active maintenance. Abandoned repositories don’t make the list, no matter how clever the idea was.
Documentation quality. If the README doesn’t explain what the tools do, it’s not making this list.
Coverage of a distinct job. We tried not to duplicate categories unless two servers genuinely serve different setups.
We’ve grouped the picks below by what they’re actually for, since “best mcp servers” as one flat list of 15 unrelated tools isn’t that useful. If you write frontend code, skip to Browser Automation. If you’re closer to platform engineering, skip to Operations and Infrastructure.
AI Context and Documentation
1. Context7
If your AI assistant keeps suggesting library APIs that don’t exist, this is why. Context7 indexes real, version-specific documentation for popular libraries and injects it into the model’s context on request, instead of relying on whatever the model memorized during training (which is often outdated by the time you’re using it).
This directly addresses one of the most common failure modes in AI-assisted coding: hallucinated APIs. Context7 provides accurate, current library documentation, which noticeably reduces the rate at which an AI client suggests methods, parameters, or imports that simply don’t exist in the version you’re using.
Best for: any coding setup, regardless of language or framework. If you install one server from this whole list, this is the one worth prioritizing.
Sequential Thinking MCP helps an AI assistant break a complex task into smaller, ordered steps, with reflection between them, instead of jumping straight to an answer. It’s a reference server from Anthropic, so it’s minimal, but it noticeably improves output quality on architectural decisions and multi-step debugging.
The main limitation is that it adds unnecessary overhead for simple, single-step tasks, so it’s worth toggling on selectively rather than leaving it active for everything.
The official GitHub MCP server changes how AI assistants work with repositories. You can search code, read and write files, review pull requests, comment on issues, manage branches, and trigger workflows, all from inside your AI client instead of switching to a browser tab.
GitHub MCP allows repository management without ever leaving your AI assistant. Community wrappers existed before this, but the official server is better scoped and more reliable, and it supports pull request automation in a way that actually holds up: reading diffs, leaving structured review comments, and tracking review status across a repository.
Setup note: you’ll need a personal access token (or a GitHub App) with the right scopes. Use a scoped API key rather than a broad, privileged token; the principle of least privilege matters more here than with most integrations, since the AI client can act on your behalf.
Best for: almost every developer, since almost everyone’s work touches GitHub in some form.
The reference implementation from Anthropic. It gives your AI client scoped, controlled read and write access to local project directories: nothing outside the folders you explicitly allow.
It’s a simple server on paper, but it’s foundational: it’s what lets an AI client actually read your codebase and write changes directly, instead of you copy-pasting code back and forth. Most modern IDEs with built-in AI features (Cursor, current Claude Code) already expose something like this internally, so the standalone server is most useful for cross-project work or non-IDE clients.
Being able to interact with your database directly from your editor, instead of switching to a separate database client, is a genuinely useful workflow. With the SQLite MCP server running, you can get your schema, insert, update, read, delete, or run custom SQL queries.
Playwright MCP has become one of the most widely used MCP servers in the developer community, and for good reason. It gives your AI agent full browser automation: navigate pages, click, fill forms, take screenshots, and run JavaScript inside a real browser session.
Playwright MCP enables browser automation for AI agents in a way that’s genuinely production-usable, not just a demo. It’s the natural pick for visual UI testing, debugging JavaScript-heavy pages, or any agentic workflow that needs to interact with a real web page the way a human would.
Best for: frontend developers doing visual testing or UI debugging, and any workflow that needs to scrape or interact with JavaScript-rendered pages specifically.
Giving an AI model direct database access is powerful and risky in equal measure, so this is worth setting up carefully. Postgres MCP translates natural language requests into SQL queries, letting your assistant inspect schemas, explore data relationships, and answer questions about production data without you writing SQL by hand.
Postgres MCP servers enable database introspection and schema evaluation through natural language, which is genuinely useful for a data or analytics-adjacent workflow. Configure read-only access first; only widen scope once you trust the specific queries the model tends to generate.
If your stack already runs on Supabase, this is the natural pick over a generic Postgres server. The official Supabase MCP server lets an AI agent query Postgres, run migrations, manage auth and storage, and read edge functions, all scoped to your project.
Best for: teams already using Supabase who want their agent to ship real database changes, not just describe them.
Building on Firebase already gives you a lot out of the box; the Firebase MCP server extends that by letting an AI assistant document database operations, manage media files, and manage users directly.
For anyone doing platform engineering or SRE work, this is close to essential. Kubernetes MCP lets an AI agent inspect and operate clusters: list pods, read logs, describe deployments, and (under tightly scoped permissions) apply manifests.
This is particularly valuable during incident response, when an agent that can pull logs and describe deployment state in natural language saves real time over manually running kubectl commands under pressure.
Security note: scope RBAC carefully. Read-only access is a reasonable default; write access to a cluster should be an explicit, deliberate decision, not a default setting.
Sentry MCP lets your AI assistant read errors, traces, and release issues directly. Paired with GitHub MCP, it enables a genuinely useful workflow: the agent reads a production error, traces it to the likely offending commit, and opens a draft fix, all in one conversation.
MCP servers like this help automate debugging by surfacing exception logs and stack traces directly in the conversation, instead of you tabbing over to a separate dashboard mid-debugging session.
Grafana is a fantastic open source platform for monitoring, analyzing, and visualizing data in real time. With the Grafana MCP server, you can search, create, update, and close incidents, query data from Prometheus and Loki, and review alerts, with more functionality being added regularly.
Best for: deployment monitoring and teams already running Grafana as their observability stack.
Remembering every Docker command and flag is its own skill; this server removes the need to. Use natural language commands to create containers, inspect running services, and debug, whether you’re a server admin or just getting started with Docker.
A privacy-first web search server that gives your AI agent live access to current information, something no model has natively past its training cutoff. Brave Search MCP is a common first pick specifically because it’s low-cost and doesn’t track queries, which matters if your agent is searching on behalf of sensitive internal work.
Best for: research-heavy agents, and any workflow where the model needs current documentation, current pricing, or current events that postdate its training data.
Stripe’s own team built this MCP server, and while they haven’t exposed their entire API surface, there’s still solid functional coverage: creating and listing products and customers, creating payment links and invoices, checking your balance, listing disputes, and reviewing subscriptions.
These aren’t strictly developer tooling, but they’re genuinely useful for a developer’s day to day, and worth knowing about even if they’re not the main reason you’re reading this list.
Markdownify. Converts files and web content (audio, websites, PowerPoint, PDFs) into clean Markdown, split into format-specific tools for better results. GitHub: https://github.com/zcaceres/markdownify-mcp
Google Workspace (Gmail and Calendar). Read, search, and answer email; manage calendar events and attendees. Built on the mcp-gsuite package name, but it connects to current Google Workspace, not the old “G Suite” branding Google retired in 2020. GitHub: https://github.com/MarkusPfundstein/mcp-gsuite
Notion. Read and write pages, databases, and properties, useful for teams that keep product or technical context in Notion instead of in-repo docs. GitHub: https://github.com/makenotion/notion-mcp-server
React Analyzer. Ask questions like “explain HardComponent to me” and get a detailed breakdown of props, structure, and usage; also useful for generating documentation. GitHub: https://github.com/azer/react-analyzer-mcp
WhatsApp. Send and search messages, list contacts, send files. Technically bridges a Python MCP server to a Go application connecting to WhatsApp’s web API. GitHub: https://github.com/lharries/whatsapp-mcp
Best MCP Servers at a Glance
Comparison of popular MCP servers by category, maintainer, and best use case
#
Server
Category
Official
Best For
1
Context7
Docs/Context
Community
Every coding setup
2
Sequential Thinking
Docs/Context
Anthropic
Complex, multi-step reasoning
3
GitHub MCP
Dev Tooling
Official
Repo, PR, and issue work
4
Filesystem
Dev Tooling
Anthropic
Local file access
5
SQLite
Dev Tooling
Community
Local-first database work
6
Playwright MCP
Browser Automation
Microsoft
Frontend/UI testing
7
Postgres MCP
Data/Backend
Community
Natural language SQL
8
Supabase MCP
Data/Backend
Official
Supabase-based stacks
9
Firebase
Data/Backend
Community
Firebase-based stacks
10
Kubernetes MCP
Ops/Infra
Community
Platform engineering, SRE
11
Sentry MCP
Ops/Infra
Official
Error triage and debugging
12
Grafana
Ops/Infra
Official
Deployment monitoring
13
Docker
Ops/Infra
Community
Container management
14
Brave Search MCP
Search
Official
Live web research
15
Stripe
Payments
Official
Billing and revenue
Common Pitfalls When Connecting Multiple MCP Servers
A few things to watch for once you’re running more than two or three of these together:
Schema management gets messy fast. Reference resolution can complicate MCP server setups once you’re combining several servers, especially when converting existing OpenAPI specs into MCP tool schemas. Handling circular dependencies requires careful planning, and converting nested references gets complex quickly if you’re building custom servers rather than using the official ones above.
MCP tools need to be self-contained. If a tool’s schema depends on external references the client can’t resolve, calls fail in ways that are hard to debug from the AI client’s side.
Security is a real, not theoretical, concern. Security considerations for MCP servers include scoped access and avoiding privileged credentials wherever possible. A GitHub token with full repo scope, a Kubernetes kubeconfig with cluster admin, a Slack token with chat:write on every channel: all of these are common, and all of them are more access than most workflows actually need.
Too many active servers hurts, not helps. Every connected server adds tools to the model’s context. Past six or seven active servers, tool selection accuracy tends to drop, and you’ll see the agent picking the wrong tool for a task it could otherwise handle correctly.
Test locally before connecting to production. Using local development stacks is recommended for testing MCP toolsets efficiently, before pointing any of these (especially Postgres, Kubernetes, or Supabase) at anything production.
FAQ
What are the best MCP servers for developers?
Context7, GitHub MCP, and Playwright MCP cover the majority of day-to-day coding workflows. For infrastructure work, add Kubernetes MCP and Sentry MCP. For database work, Postgres MCP or Supabase MCP, depending on your stack.
What is an MCP server?
An MCP server is a program that exposes tools, data, or actions to an AI assistant through the Model Context Protocol, a standardized interface that lets the assistant call real systems (a database, a repository, a browser) instead of only generating text.
How do MCP servers work?
An AI client connects to the server (locally or over HTTP), the server advertises its available tools, and the client can call them with structured arguments. The server executes the action against the real system and returns a structured result the model can use.
How do I install an MCP server?
Most are installed as NPM packages or Docker containers, then referenced in your AI client’s config file (for example, claude_desktop_config.json), specifying the command to run and any required environment variables like an API key or personal access token.
Are MCP servers secure for production use?
They can be, if you follow the same access principles you’d apply to any automated system: scoped API keys instead of broad tokens, read-only access by default, and explicit review before granting write access to anything production.
What’s the difference between GitHub MCP and just using GitHub Actions?
GitHub MCP lets your AI assistant read and act on repositories conversationally, in real time, inside your AI client. GitHub Actions is CI/CD automation triggered by repository events. They’re complementary: a well-set-up workflow can use GitHub MCP for interactive review and exploration, and Actions for automated pipelines.
Can I run multiple MCP servers at once?
Yes, and most real workflows do. Just keep the active set small (three to six is a reasonable range) since every additional server adds tools to the model’s context and can reduce tool selection accuracy.
Do I need a gateway to manage multiple MCP servers?
Not for a handful of personal servers. Once you’re running many MCP servers across a team, with different access levels and compliance requirements, a control plane becomes worth it. Obot provides a unified control plane for managing MCP servers, enforcing access policy, and maintaining audit logs for compliance.
That’s our current list of the best MCP servers for developers. Categories and specific tools will keep shifting as the ecosystem matures, but the core categories (context and documentation, dev tooling, browser automation, data, infrastructure) are a solid map for deciding what to install first.
If you’re looking for a way to manage and scale MCP servers across a team rather than just your own machine, try Obot: visit our GitHub to get started.
