How to choose an MCP server

An MCP server gives an AI assistant (Claude, Cursor, VS Code, and others) a set of tools for one system: a code host, a database, a browser, a file folder. Before comparing servers, write down the one or two things you want the assistant to do, such as "read open pull requests" or "query our staging Postgres". That tells you which system you need, and whether you need read access only or write access too.

Then shortlist candidates by category or use case. For popular systems there are often several servers; the compare pages put two side by side.

If the company that runs the system publishes its own MCP server, start there. It is more likely to track API changes, use the right authentication flow, and be fixed quickly when something breaks. Community servers are worth considering when they cover something the official one does not, or when there is no official one.

The MCP specification and client apps are still evolving. A server with no commits for a year may not work with current clients. On each server page, look at:

• the last commit date — recent activity is a good sign;
• open issues relative to stars — many unanswered issues suggest an abandoned project;
• the change history, which shows how the project has moved over time.

The recently updated ranking is a quick way to find active projects.

This is the most important step. A server runs with whatever access you give it: your API token, your files, your database. Ask:

• Does it need a token, and what scopes? Prefer servers that work with read-only or narrowly scoped tokens.
• Can it write, send, or delete? If you only need to read, look for a read-only mode or a server that only reads.
• Does it touch the local filesystem? Check that you can limit it to specific folders.

Each server page shows the permission signals we detected and a risk level. The security guide explains how to read them.

• Runtime: servers start through npx (Node.js), uvx or pip (Python), Docker, or a hosted URL. Pick one that matches what you already have installed.
• Client: confirm the README has configuration for your client. Most use the same mcpServers JSON block, but details such as environment variables and paths differ.
• Local or remote: a local server runs on your machine with your permissions; a remote server keeps your data flowing through someone else's infrastructure. Both are fine — just know which one you are choosing.

Good documentation is a strong predictor of a good experience. Look for a clear list of tools, a copy-paste configuration example, a description of every environment variable, and a note on security or permissions. Our benchmark docs score checks for exactly these items; see the best documented ranking.

Every tool a server exposes is described to the model on each request. A server with dozens of tools uses more context and gives the model more chances to pick the wrong one. If you only need a few operations, a smaller, focused server — or one that lets you disable tools — usually works better. The most tools ranking shows tool counts we measured.

• The server covers the exact job you wrote down.
• It is official, or clearly better than the official option.
• It had commits in the last few months.
• You know which credentials it needs and they can be narrowly scoped.
• Write or delete actions are either needed or can be turned off.
• It runs on a runtime and client you already use.
• The README documents tools, configuration, and environment variables.

Start with the full server list, or see what the data shows about the ecosystem as a whole.