# punkpeye/awesome-mcp-servers: a link list with a local-versus-cloud rule > A curated list of Model Context Protocol servers, sorted into about fifty categories and tagged by language, scope, and operating system. The list's real editorial decision is the line it draws between servers you run against local software and servers that call remote APIs. **punkpeye/awesome-mcp-servers** — A collection of MCP servers. - Repository: https://github.com/punkpeye/awesome-mcp-servers - Website: https://glama.ai/mcp/servers - Stars: 95,673 · Forks: 16,804 - Language: Unknown - License: MIT - Published: 2026-08-17 · Updated: 2026-08-18 · Language: en - Canonical page: https://hysenlabs.com/projects/punkpeye-awesome-mcp-servers ## What is MCP, in the list's own framing The list opens by defining its subject. MCP is described as an open protocol that lets AI models interact with local and remote resources through standardized server implementations, and the list says it focuses on production-ready and experimental servers that extend AI capabilities through file access, database connections, API integrations, and other contextual services. Consequence for the reader: the list covers two maturity levels at once, servers considered production-ready and servers still experimental, and it does not separate them into different sections, so the curation is by category and not by trust. ## Fifty categories is an index, not a tutorial The Server Implementations section is split into roughly fifty categories, from Aggregators and Browser Automation through Databases, Security, and Version Control, down to Other Tools and Integrations. Each category carries a one-line description of what that kind of server is for. Consequence for the reader: this is a lookup table, not a path through learning the protocol. Nothing here walks you from a first server to a working setup, and the sheer number of categories means you will land in the same place a directory search would have taken you, only with a short gloss attached to each group. The category names are also domain-shaped rather than tool-shaped, so a developer looking for a database tool and a data scientist looking for the same capability browse different folders that happen to hold overlapping servers, and the list never tells you which of the two folders is more complete for what you need. Aggregators, the first category, are a case in point: they promise one server that reaches many apps and tools, which is a different proposition from a single-purpose entry buried further down. ## Local and cloud are not the same thing, and the list says why Every entry is tagged with a scope marker, and the list includes a note explaining the distinction rather than leaving you to guess. It says to use local when the server is talking to locally installed software, giving control over a Chrome browser as the example, and to use cloud when the server is talking to remote APIs, a weather API as the example. Consequence for the reader: the tag is a security question in disguise. A local server reaches into software and data already on your machine, while a cloud server sends your request to someone else's endpoint, and the two deserve different levels of trust before you wire either into an agent that can act. ## Language and operating system travel with every link Each server is also marked for its codebase language and the operating systems it targets. The legend names Python, TypeScript or JavaScript, Go, Rust, C#, Java, C or C++, and Ruby, and separate markers cover macOS, Windows, and Linux. Consequence for the reader: you can filter the list down to the stack you already maintain before you read a single server's documentation, which matters when you are deciding whether a tool is worth reading the source of. The language marker also tells you what you are committing to review, since a server is a program you are about to give an agent permission to call. The language list is not exhaustive, so a server written in something outside those eight carries no marker rather than an unfamiliar one, and you have to open its repository to learn what it is. The same goes for the operating system tags: a missing marker means unstated, not cross-platform, and the list does not tell you which of the two it is. ## The list points at a separate directory for hosted servers There is a deliberate split between what this list holds and what it defers. The Remote Servers section states that this list is for servers with a GitHub repository that you install and run yourself, and that if you want a hosted server you connect to over a URL you should see awesome-remote-mcp-servers. Clients are also deferred, to awesome-mcp-clients and to a glama.ai page. Consequence for the reader: the self-hosted framing is a real editorial boundary, so if your goal is to connect a URL and start working, this is the wrong list and the sibling one is the right place, which is a distinction you would not infer from a directory of hundreds of links. ## A synced web directory sits behind the Markdown The Server Implementations section carries a note that there is a web-based directory, hosted on glama.ai, that is synced with the repository. The repository's own homepage is that directory, and several entries carry a second link next to the GitHub URL pointing at a per-server page on the same site. Consequence for the reader: the Markdown file in the repository and the directory are meant to agree, so browsing the site and reading the repository are two views of one list rather than two independent catalogs. That also means an entry you find on the site has a source you can point at, and an entry you read in the repository should have a page you can open. The list also links a community Discord and an r/mcp subreddit, so disagreements about entries have somewhere to go besides the commit history. The category counts are not stated anywhere, so the size of any one group is something you learn by opening it. ## This is a documentation repository, not a code package What the repository actually contains explains what it can and cannot do for you. The top-level entries are a set of translated README files, README.md alongside versions in Thai, Chinese Simplified and Traditional, Japanese, Korean, and Persian, plus LICENSE, CONTRIBUTING.md, and .github. There is no source directory, no package manifest, and no build. The repository has no GitHub releases, and its primary language is not even recorded. Consequence for the reader: there is nothing to install and nothing to run, so the list can only point you at a server. Whatever a server does, you learn from that server's own repository, and the contribution guide is how an entry gets added, not how code gets deployed. The translated READMEs are the only substantive files, which is also a hint about how you would read this list if you do not read English, since the same categorized structure is carried in each language file rather than left to a translation service. ## Conclusion This list fits a reader who already knows the Model Context Protocol and needs to find a server for a specific job, then wants to judge it by whether it drives local software or a remote API before reading its own docs. It does not fit someone learning what MCP is, because the list deliberately points elsewhere for the background, and it is not a package you can install, since the repository is documentation and link files rather than runnable code. Before you adopt any server it points you to, read that server's own repository, and use the list's local-versus-cloud tag as the first filter, because a local server reaches into software on your machine while a cloud server sends your data to a remote API. ## FAQ ### What do MCP servers actually do? MCP is an open protocol that lets AI models interact with local and remote resources through standardized server implementations. Servers extend a model's capabilities through things like file access, database connections, and API integrations. ### Is an MCP server a real server? In this list, an MCP server is something with a GitHub repository that you install and run yourself. If you want a hosted server you connect to over a URL instead, the list points you to a separate awesome-remote-mcp-servers collection. ### Is it safe to use MCP servers? The list itself does not make a safety claim, but its tagging gives you the raw material for one. It marks each server as local or cloud, and explains that a local server talks to locally installed software while a cloud server talks to remote APIs, which is the distinction to weigh before you let a server act on your behalf. ### find awesome mcp servers and clients This repository holds the servers, grouped into about fifty categories and tagged by language, scope, and operating system. For the client side, the list sends you to awesome-mcp-clients and to a glama.ai clients page. ### Why would I want an MCP server? The list frames MCP servers as a way for an AI model to reach contextual services it otherwise cannot touch, through file access, database connections, and API integrations. Each server connects one such capability, and the local-versus-cloud tag tells you whether it acts on your machine or on a remote endpoint. ## Sources - [Official documentation](https://glama.ai/mcp/servers) - [Official README](https://github.com/punkpeye/awesome-mcp-servers#readme) - [Project repository](https://github.com/punkpeye/awesome-mcp-servers) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/punkpeye-awesome-mcp-servers