MCP GitHub Stars Benchmark 2026: The Median Listed Server Has 250 Stars

GitHub popularity is sharply concentrated in MCPtrove's directory snapshot: the median listing has 250 stars, while the 90th percentile has 3,335.

Contents

• Methodology and limitations
• Main findings
• Interpretation
• Practical decision guidance
• How to cite and download the data
• Related MCPtrove research
• FAQ

Methodology and limitations

This benchmark describes the GitHub-star distribution across 520 listings in an MCPtrove directory snapshot. The unit of analysis is the directory listing, not necessarily a unique GitHub repository. MCPtrove collected the github_stars value associated with each listing and calculated summary statistics over the sorted field.

Percentiles use nearest-rank positions. That makes the results straightforward to reproduce: sort the observed star counts, then select the value at the relevant rank. The benchmark reports the median, upper-quartile point, 90th percentile, 95th percentile, and maximum.

A listing can expose many tools. GitHub stars therefore describe attention around the listed project, while tool count describes the functional surface exposed through that listing. They answer different questions and should not be treated as interchangeable.

Category, authentication, transport, official, and verified labels are MCPtrove-normalized fields where relevant. Those labels make directory-level comparisons more consistent, but they do not transform a listing into an independent audit of the underlying project.

The most important limitation is duplication. Multiple directory listings can point to the same repository, so the 1,321,558 summed stars represent listing-level exposure rather than a unique-repository audience. The result is also a popularity snapshot, not a quality, security, maintenance, or adoption score.

This page does not claim that MCPtrove surveyed users or measured every MCP server in existence. It describes only the listings present in the supplied snapshot.

Main findings

The distribution is highly uneven. A typical listing sits far below the upper tail, while a small set of highly visible projects accounts for a disproportionate share of observed stars.

The distance between 250 median stars and 3,335 stars at the 90th percentile is the central fact in this benchmark. The median is useful for describing an ordinary listed server. It is not a reasonable proxy for the visibility of the most prominent projects.

The highest-star listings include the following projects:

The concentration also shows why threshold-based summaries need context. There are 369 listings at 100+ stars, but only 114 listings at 1,000+ stars. Crossing the first threshold indicates some visible community attention; crossing the second places a listing in a much narrower part of the observed distribution.

Interpretation

GitHub stars are useful as a discovery signal. A project with substantial public attention may have more examples, more community discussion, better documentation, or a larger pool of people who have encountered the same setup. Those are practical advantages when evaluating unfamiliar infrastructure.

But stars are a terrible single-variable procurement rule. They do not tell you whether a server requests excessive permissions, handles credentials safely, supports the transport you need, or remains actively maintained. They also do not tell you whether its tools fit your workflow. A popular server can be a poor match for a narrowly defined task, while a less visible project can be exactly right for a controlled deployment.

The listing-level limitation matters as well. If several directory entries point to the same repository, summing their stars can make the directory’s aggregate exposure look larger without representing additional independent projects. Treat the total as a property of this directory view, not as a count of unique communities or users.

Most importantly, the benchmark does not establish causation. A higher star count may coincide with stronger documentation, broader distribution, official backing, or a compelling use case, but this dataset does not show that stars caused any of those outcomes.

Practical decision guidance

Use the benchmark to set expectations, not to make the final decision.

Start with percentiles. The median gives you a grounded reference for what a typical listed server looks like in this snapshot. The upper-tail values help identify projects that have attracted unusually strong public attention. This is a useful first filter when the candidate set is large.

Then inspect the project itself:

• Check recent maintenance activity, release cadence, issue handling, and documentation quality.
• Review the tools exposed by the server, including what data they can read, modify, transmit, or execute.
• Examine authentication requirements and permission boundaries before connecting real accounts or production data.
• Confirm that the transport matches your client, deployment environment, and operational constraints.
• Treat official and verified labels as useful directory context, not as substitutes for your own security review.
• Test the actual configuration with representative tasks rather than assuming that popularity implies compatibility.

For a faster starting point, use MCPtrove’s curated shortlist of the best MCP servers. Once you have a concrete candidate, use the configuration doctor to check a real configuration and identify setup issues.

A strong procurement decision combines popularity context with maintenance evidence, permission analysis, transport compatibility, and tool-level fit. Stars can help you decide what to inspect first. They should not decide what you deploy.

How to cite and download the data

The complete source file is available as the raw CSV dataset. Use it to reproduce the summary statistics, inspect individual listing-level values, or build a different view of the distribution.

Citation: MCPtrove, “MCP GitHub Stars Benchmark 2026: The Median Listed Server Has 250 Stars,” https://mcptrove.com/research/mcp-github-stars-benchmark, accessed September 23, 2026.

Related MCPtrove research

For adjacent context, see MCPtrove’s maintenance activity statistics, official and verified statistics, and tool count benchmark. Together, these pages help separate community attention from maintenance, provenance, and functional breadth.

FAQ

Does a high star count guarantee quality?

No. Stars measure public GitHub attention, not security, reliability, maintenance, or suitability for a particular workflow.

Why are the results described as listing-level?

Because the directory can contain multiple listings that point to the same repository. The benchmark therefore summarizes the records in the directory snapshot rather than claiming a unique-repository count.

How should I use the percentile values?

Use them as context for comparing candidates within this snapshot. A percentile can show whether a listing is typical or unusually visible, but it cannot determine whether the server is appropriate for your environment.

What should I check after finding a popular server?

Review maintenance, permissions, authentication, transport, exposed tools, documentation, and configuration behavior. Then test the server with the data and workflows it will actually handle.