10 MCP Servers Every Developer Should Know in 2026
A practical guide to the MCP servers that actually matter. For each one: what it does, when to use it, and how to get started in under a minute.
Why These Ten?
There are over 4,000 MCP servers in the wild. Most of them are experiments. Some are duplicates. A few are genuinely transformative.
This list focuses on the MCP servers that solve real, recurring problems — the ones that, once installed, you will wonder how you worked without. Each server listed here has strong maintenance scores, clear documentation, and a practical reason to exist.
For each server, we include what it does in plain English, a realistic use case, and how to get it running. All of these work with Claude Desktop, Cursor, Windsurf, and most MCP-compatible clients.
1. Filesystem
What it does: Gives your AI agent the ability to read, write, create, and manage files on your local machine — scoped to directories you explicitly allow.
Use case: You are working on a project and ask Claude to "create a new React component called UserProfile with these props." Instead of copying and pasting, the agent writes the file directly into your src/components/ directory. It can also read your existing code to understand project conventions before generating new files.
Setup: Add to your MCP client config with an allowedDirectories parameter pointing to your project folders. Never grant access to your home directory or system paths.
2. PostgreSQL
What it does: Connects your AI agent directly to a PostgreSQL database. It can run queries, describe schemas, list tables, and explain query plans.
Use case: You are debugging a production issue and need to check whether a specific user's records are consistent across three tables. Instead of writing the JOIN yourself, you describe the problem in natural language. The agent writes the query, runs it, and summarizes the results.
Setup: Provide a connection string (ideally read-only for safety). Works with Supabase, Neon, RDS, or any Postgres instance.
3. GitHub
What it does: Full GitHub integration — create issues, open pull requests, search repositories, review code, manage branches, and read file contents from any repo you have access to.
Use case: You tell your agent: "Create an issue for the login timeout bug we discussed, assign it to the backend team, and label it priority-high." Done in seconds, without opening a browser. Even better for code review: the agent can read a PR diff and provide structured feedback.
Setup: Requires a GitHub personal access token with the scopes you want to grant (typically repo and issues).
4. Brave Search
What it does: Lets your AI agent search the web using the Brave Search API, returning current results with titles, URLs, and snippets.
Use case: You are researching a new library and ask: "What are the known issues with Prisma 6?" The agent searches the web, finds recent GitHub issues, blog posts, and Stack Overflow threads, and synthesizes an answer with sources — all without leaving your editor.
Setup: Requires a free Brave Search API key (generous free tier). Set it as an environment variable in your MCP config.
5. Puppeteer
What it does: Gives your AI agent a headless browser. It can navigate to URLs, take screenshots, click elements, fill forms, and extract page content.
Use case: You need to test that your login flow works after a deploy. The agent navigates to your staging URL, fills in test credentials, clicks submit, and screenshots the resulting dashboard — then tells you whether the expected elements are present.
Setup: Install via npm. Puppeteer downloads its own Chromium binary, so no browser installation is needed. Be mindful of memory usage on constrained machines.
6. Slack
What it does: Connects your AI agent to Slack workspaces. It can read channels, post messages, search message history, and manage threads.
Use case: After a deployment, you ask the agent to "post a summary of today's changes to #engineering with the list of PRs merged since yesterday." The agent pulls the info from GitHub (if you also have the GitHub server), formats it, and posts to Slack — a workflow that normally takes ten minutes reduced to one sentence.
Setup: Requires a Slack Bot Token with appropriate scopes. Create a Slack app in your workspace's API settings and add the token to your MCP config.
7. Sequential Thinking
What it does: A reasoning tool that helps AI agents break complex problems into structured, step-by-step thought processes. It maintains a chain of reasoning that can branch, revise, and backtrack.
Use case: You ask the agent to "design a database schema for a multi-tenant SaaS application with role-based access control." Instead of jumping to a solution, Sequential Thinking forces a structured approach: define requirements, identify entities, map relationships, consider edge cases, then propose the schema. The result is dramatically more thorough than a single-shot response.
Setup: No external dependencies or API keys. Install and add to your config — it is a pure reasoning tool.
8. Memory
What it does: Provides persistent memory across conversations using a knowledge graph. Your agent can store facts, relationships, and context that survive between sessions.
Use case: You tell your agent your project conventions: "We use kebab-case for file names, Zod for validation, and never use default exports." The Memory server stores these preferences. In future sessions, the agent recalls them automatically — no more repeating your standards in every conversation.
Setup: Install and configure a storage path for the knowledge graph file. The data persists locally on your machine.
9. Google Maps
What it does: Provides access to the Google Maps Platform APIs — geocoding, directions, place search, distance calculations, and elevation data.
Use case: You are building a delivery logistics feature and need to calculate drive times between 15 warehouse locations and 50 delivery addresses. Instead of writing API integration code, you describe the matrix and the agent computes it using the Distance Matrix API, returning a formatted table you can pipe into your application.
Setup: Requires a Google Maps API key with the relevant APIs enabled (Geocoding, Directions, Places). Pay-as-you-go pricing applies, but the free tier covers most development use.
10. Fetch
What it does: A simple but essential tool: lets your AI agent make HTTP requests to any URL and process the response. Supports GET, POST, PUT, DELETE with custom headers and bodies.
Use case: You want to check whether your production API is returning the expected response for a specific endpoint. The agent makes the request, parses the JSON response, and compares it against your expected schema — all described in natural language. Also invaluable for interacting with any REST API that does not have a dedicated MCP server yet.
Setup: Minimal configuration required. Install and add to your config. Consider setting allowed URL patterns if you want to restrict which domains the agent can access.
Getting Started
You do not need all ten. Start with the servers that match your daily workflow:
- If you write code all day: Filesystem + GitHub + Sequential Thinking
- If you manage data: PostgreSQL + Fetch + Memory
- If you do research: Brave Search + Fetch + Puppeteer
- If you coordinate a team: Slack + GitHub + Memory
Each server installs independently and can be added or removed from your MCP config at any time. Start small, add when you feel friction, and check our scoring page to verify the security posture of anything you install.
Browse all 4,133 MCP servers in the directory to find tools specific to your stack.
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