How to Choose an MCP Server: A Practical Evaluation Checklist

There is no shortage of MCP servers. The hard part is picking ones you can trust. AgentIndex currently lists 1,389 open-source tools related to the Model Context Protocol, and the distribution is very uneven: 820 of them (59%) have between 10 and 100 GitHub stars, while only 138 have more than 1,000. Many are weekend experiments; a few are maintained by the companies whose products they connect to.

Because an MCP server acts with real permissions on your machine or your accounts, choosing one deserves the same care as adding a dependency to production code. This guide gives you a checklist to run through before connecting a server, then lists established options by use case. If you are new to MCP, start with What Is MCP?.

The checklist

1. Who publishes it?

Prefer servers published by the vendor of the service they connect to. The GitHub MCP Server is maintained by GitHub, Playwright MCP by Microsoft, Chrome DevTools MCP by the Chrome DevTools team, and the Brave Search MCP server by Brave. A first-party server tracks API changes faster and is more likely to handle authentication correctly.

Community servers are often excellent, especially for niche services, but check the repository owner, the contributor list and whether issues get answered.

2. Is it actively maintained?

MCP and the clients that use it are still evolving, so an abandoned server breaks sooner than an abandoned library. In the directory, 1,015 of the 1,389 MCP-related tools received an update in the last 30 days. Look at the date of the last commit and release, and whether recent issues mention incompatibility with current clients. A server that has not been touched for months is not necessarily broken, but you should test it before relying on it.

3. What can it actually do?

Read the tool list before installing. Separate read operations from write operations: "search issues" and "read file" are very different from "merge pull request" or "run shell command". Good servers let you narrow this down. The GitHub MCP Server, for example, has a --read-only flag and a --toolsets option to expose only the groups of tools you need. If a server offers broad write or command execution access with no way to restrict it, treat that as a cost, not a feature.

4. Local or remote?

A stdio server runs as a local process with your user's access to files, network and credentials on disk. A remote (HTTP) server runs elsewhere and receives whatever your agent sends it. Neither is automatically safer:

• Local servers keep data on your machine but can do anything your user account can do.
• Remote servers are isolated from your machine but see your queries and any content passed to them.

Choose based on what the server needs. A browser automation server must run locally; a documentation lookup service like Context7 works well remotely.

5. How does it handle credentials?

Check how you are expected to provide API keys or tokens. OAuth flows and environment variables are better than pasting secrets into a configuration file that might be committed to git. Prefer tokens with the narrowest scope the server needs: a GitHub token limited to specific repositories, or a read-only database user.

6. Is the license clear?

Of the 1,389 MCP-related tools in the directory, 100 have no license at all and another 106 use a license GitHub cannot identify. Without a license you technically have no right to use or modify the code. For anything beyond personal experiments, prefer servers under common open-source licenses such as MIT or Apache-2.0, and read the terms if a server uses a copyleft license like AGPL.

7. What does it cost in context?

Every connected server adds its tool names and descriptions to the model's context on each request. A server with dozens of tools can crowd out your actual task and make the model pick the wrong tool. Prefer servers with focused tool sets, use options that limit exposed tools, and disconnect servers you are not using. For coding agents, a command-line tool plus a skill can be cheaper still; the Playwright team recommends that route for high-throughput coding agents.

Established picks by use case

The servers below are widely used, actively maintained as of this writing and published by a known organisation. Star counts are from the AgentIndex directory.

Star counts tell you about popularity, not safety. A popular server with broad permissions still deserves the checklist above.

Two useful helpers

• mcp-remote connects a client that only supports local (stdio) servers to a remote MCP server, which is handy for older clients.
• FastMCP is a Python framework for building MCP servers and clients. If no existing server fits, writing a small focused one is often safer than adopting a sprawling community server.

A quick decision routine

1. Search the MCP Servers category and the relevant functional category for an official server first.
2. Run the checklist: publisher, maintenance, tool list, transport, credentials, license, context cost.
3. Start with read-only access and a narrowly scoped token.
4. Test on a throwaway project or account before connecting it to real data.
5. Review the server again when you update it; new versions can add tools and permissions.

A handful of well-chosen servers will do more for your agent than a long list of unvetted ones.