Top 10 MCP Servers Every Developer Should Know

The MCP ecosystem has grown quickly, and it can be hard to know which servers are worth your time. This guide covers the ten servers that show up most often in real developer workflows — what each one does, the tools it exposes, and the situations where it shines.

1. filesystem-server

Install: npx -y @modelcontextprotocol/server-filesystem

The filesystem server gives an AI assistant direct access to directories on your local machine. It is usually the first server developers add because so many tasks involve reading or writing files.

Key tools

• read_file — reads a single file's contents
• write_file — creates or overwrites a file
• list_directory — returns directory contents
• search_files — finds files matching a pattern
• move_file / delete_file — file management operations

When to use it

Any time you want an AI to read your codebase, generate files in a project, or reorganize a directory structure without copy-pasting content back and forth. Pair it with a code-editing tool like Cursor for a tight feedback loop.

Configuration note: Pass the directories you want to expose as arguments. Never expose your entire home directory — use the minimum set of paths needed.

---

2. github-server

Install: npx -y @modelcontextprotocol/server-github

The GitHub server wraps the GitHub REST API so an AI can read and write to repositories on your behalf. It covers the operations developers perform dozens of times a day: reading issues, opening PRs, searching code.

Key tools

• get_file_contents — fetch file content from a repo
• search_code — full-text code search across GitHub
• create_issue / update_issue — issue management
• create_pull_request — open a PR with a title and body
• list_commits / get_commit — commit history

When to use it

Triaging issues, drafting PR descriptions, searching an unfamiliar open-source codebase, or automating release note generation. Requires a GITHUB_PERSONAL_ACCESS_TOKEN environment variable with appropriate scopes.

---

3. postgres-server

Install: npx -y @modelcontextprotocol/server-postgres

The Postgres server connects to a PostgreSQL database and lets an AI run read-only queries, inspect schemas, and reason about your data model.

Key tools

• query — executes a SQL SELECT and returns results
• Schema inspection via resources: tables, columns, foreign keys, indexes

When to use it

Writing complex queries, debugging data quality issues, generating documentation from a live schema, or exploring a new codebase's data model. The server enforces read-only access by default, which makes it safe to use against a staging database.

Configuration: Set the POSTGRES_URL environment variable to your connection string.

---

4. puppeteer-server

Install: npx -y @modelcontextprotocol/server-puppeteer

The Puppeteer server launches a headless Chromium browser that an AI can control programmatically. It enables web scraping, screenshot capture, and automated interaction with web UIs.

Key tools

• puppeteer_navigate — navigate to a URL
• puppeteer_screenshot — capture a screenshot
• puppeteer_click / puppeteer_type — interact with page elements
• puppeteer_evaluate — run arbitrary JavaScript in the browser context
• puppeteer_select — interact with dropdown menus

When to use it

Scraping websites that require JavaScript rendering, testing web UIs, extracting data from SaaS dashboards, or taking screenshots for documentation. Puppeteer runs locally so there are no external service costs.

---

5. slack-server

Install: npx -y @modelcontextprotocol/server-slack

The Slack server connects to a Slack workspace and lets an AI read channel history, post messages, and manage conversations.

Key tools

• slack_list_channels — list available channels
• slack_get_channel_history — fetch recent messages from a channel
• slack_post_message — send a message
• slack_reply_to_thread — post a thread reply
• slack_get_users — list workspace members

When to use it

Drafting team updates, summarizing a long thread, posting build notifications, or searching for context buried in channel history. Requires a Slack bot token (SLACK_BOT_TOKEN) with the relevant OAuth scopes.

---

6. fetch-server

Install: uvx mcp-server-fetch

The fetch server makes HTTP requests from the AI's perspective, retrieving web pages and converting them to Markdown for easier consumption. It is lightweight and has no external dependencies beyond the Python runtime.

Key tools

• fetch — retrieves a URL and returns the page as clean Markdown

When to use it

Pulling documentation directly from the web, reading changelogs, or letting an AI verify something by visiting a URL. When you need a quick "go look at this page" capability without setting up a full browser automation stack, fetch is the right tool.

---

7. memory-server

Install: npx -y @modelcontextprotocol/server-memory

The memory server gives an AI persistent, structured memory across sessions using a knowledge graph stored locally. Entities, relations, and observations are stored on disk and available in future conversations.

Key tools

• create_entities — add named entities to memory
• create_relations — define relationships between entities
• add_observations — attach facts to existing entities
• search_nodes — query the knowledge graph
• read_graph — return the full graph

When to use it

Long-running projects where the AI needs to remember decisions, architecture choices, or team member preferences across sessions. Particularly useful in agentic workflows where state must persist between tool calls.

---

8. brave-search-server

Install: npx -y @modelcontextprotocol/server-brave-search

The Brave Search server exposes the Brave Search API, giving an AI real-time access to web search results without going through a browser.

Key tools

• brave_web_search — keyword search returning titles, URLs, and snippets
• brave_local_search — location-aware search for local businesses

When to use it

Answering questions about recent events, verifying that a library still exists, or finding documentation that the AI's training data might not cover. Requires a BRAVE_API_KEY from the Brave Search developer portal. The free tier includes 2,000 queries per month.

---

9. notion-server

Install: npx -y @modelcontextprotocol/server-notion

The Notion server integrates with Notion's API to read pages and databases, create new content, and update existing records.

Key tools

• notion_retrieve_page — fetch a page by ID
• notion_query_database — query a Notion database with filters
• notion_create_page — create a new page in a workspace
• notion_update_page — update page properties
• notion_search — full-text search across a workspace

When to use it

Teams that use Notion as a knowledge base or project tracker. An AI can pull spec documents during code generation, log decisions back to a project page, or summarize a database of tasks. Requires a NOTION_API_TOKEN and the integration must be added to the pages it needs to access.

---

10. sequential-thinking-server

Install: npx -y @modelcontextprotocol/server-sequential-thinking

Sequential thinking is different from the other nine servers here: it does not integrate with an external service. Instead, it gives an AI a structured tool for breaking complex problems into explicit reasoning steps.

Key tools

• sequentialthinking — iteratively builds a chain of reasoning steps, with the ability to branch, revise, and flag when more steps are needed

When to use it

Complex planning tasks, debugging sessions with multiple hypotheses, or any situation where you want the AI to show its work rather than jumping to a conclusion. It improves output quality on hard problems by forcing structured reasoning before an answer is committed.

---

Choosing the Right Servers

Most developers start with three servers: filesystem-server for local file access, github-server for repository operations, and fetch-server for pulling in documentation. From there, add servers that match your specific stack — postgres-server if you work with databases, slack-server if your team lives in Slack, notion-server if that is your knowledge base.

The sequential-thinking-server is worth adding for any AI workflow that involves planning or debugging — it costs nothing and consistently improves reasoning quality on complex tasks.

Each server installs in under five minutes. Start with one, get comfortable with how it behaves, and add more as you find gaps in what your AI assistant can do.