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modelcontextprotocol
servers
TypeScript2024–2026
modelcontextprotocol / servers

servers

Model Context Protocol Reference Servers

AI & Machine LearningSystems & InfrastructureLearning & Reference
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33Distinct people
41Mentions
1Explicit recommenders
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REPOSITORY AT A GLANCE

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What it is

Model Context Protocol Reference Servers

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

Quite telling -- thanks for the insightful comment as always, Simon. Didn't know that, even though I've been discussing this on and off all day on Reddit. He's a smart man with…

bbor used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.

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Also in these discussions

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 11:56:56 AM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month41 dated mentions

Observed mentions: 2024-11-25 17:28:18 UTC to 2026-09-04 02:23:20 UTC.

02468102024-11-01 00:00:00 UTC to 2024-12-01 00:00:00 UTC (exclusive): 5 mentions; 0 submissions, 5 commentsNov 20242024-12-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2025-01-01 00:00:00 UTC to 2025-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-02-01 00:00:00 UTC to 2025-03-01 00:00:00 UTC (exclusive): 4 mentions; 1 submissions, 3 comments2025-03-01 00:00:00 UTC to 2025-04-01 00:00:00 UTC (exclusive): 9 mentions; 1 submissions, 8 comments2025-04-01 00:00:00 UTC to 2025-05-01 00:00:00 UTC (exclusive): 8 mentions; 1 submissions, 7 comments2025-05-01 00:00:00 UTC to 2025-06-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2025-06-01 00:00:00 UTC to 2025-07-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2025-07-01 00:00:00 UTC to 2025-08-01 00:00:00 UTC (exclusive): 3 mentions; 2 submissions, 1 comments2025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-10-01 00:00:00 UTC to 2025-11-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsOct 20252025-11-01 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-12-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2026-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-02-01 00:00:00 UTC to 2026-03-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-03-01 00:00:00 UTC to 2026-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-04-01 00:00:00 UTC to 2026-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-05-01 00:00:00 UTC to 2026-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-06-01 00:00:00 UTC to 2026-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-01 00:00:00 UTC to 2026-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-08-01 00:00:00 UTC to 2026-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-09-01 00:00:00 UTC to 2026-10-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsSep 2026

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Nov 20242024-11-01 00:00:00 UTC2024-12-01 00:00:00 UTC505
Dec 20242024-12-01 00:00:00 UTC2025-01-01 00:00:00 UTC202
Jan 20252025-01-01 00:00:00 UTC2025-02-01 00:00:00 UTC000
Feb 20252025-02-01 00:00:00 UTC2025-03-01 00:00:00 UTC413
Mar 20252025-03-01 00:00:00 UTC2025-04-01 00:00:00 UTC918
Apr 20252025-04-01 00:00:00 UTC2025-05-01 00:00:00 UTC817
May 20252025-05-01 00:00:00 UTC2025-06-01 00:00:00 UTC202
Jun 20252025-06-01 00:00:00 UTC2025-07-01 00:00:00 UTC202
Jul 20252025-07-01 00:00:00 UTC2025-08-01 00:00:00 UTC321
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC000
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC101
Oct 20252025-10-01 00:00:00 UTC2025-11-01 00:00:00 UTC101
Nov 20252025-11-01 00:00:00 UTC2025-12-01 00:00:00 UTC101
Dec 20252025-12-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
Jan 20262026-01-01 00:00:00 UTC2026-02-01 00:00:00 UTC000
Feb 20262026-02-01 00:00:00 UTC2026-03-01 00:00:00 UTC101
Mar 20262026-03-01 00:00:00 UTC2026-04-01 00:00:00 UTC101
Apr 20262026-04-01 00:00:00 UTC2026-05-01 00:00:00 UTC000
May 20262026-05-01 00:00:00 UTC2026-06-01 00:00:00 UTC000
Jun 20262026-06-01 00:00:00 UTC2026-07-01 00:00:00 UTC000
Jul 20262026-07-01 00:00:00 UTC2026-08-01 00:00:00 UTC000
Aug 20262026-08-01 00:00:00 UTC2026-09-01 00:00:00 UTC000
Sep 20262026-09-01 00:00:00 UTC2026-10-01 00:00:00 UTC101

What Hacker News says

41 sources
ma2kx·Sep 4, 2026
In general the advantage of MCP would be the possibility for a fine grained control over the tools the agent is allowed to use. But unfortunately there is a myriad of nightmarish awful mcp servers around which are worse than direct API access or even a cli integration. I wont advertise any commercial mcp I use but to give an example for a well designed and useful mcp server I could name the nixos mcp. Its useful because it bundles all the nix resources to one endpoint which is more efficient than web search and gives you better control over the sources. https://github.com/utensils/mcp-nixos Another one would be this filesystem mcp which is in my opinion to prefer over direct cli access. Of course this depends also on your general sandbox strategy but if you just use a generic docker image there are still many potentially dangerous binarie
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s available and such an mcp can restrict the models capabilities. https://github.com/modelcontextprotocol/servers/tree/main/sr... And of course there are many service provider offering their mcp with its own llm / agent behind e.g. most web search provider. In this case you most likely already use an mcp without noticing it.
In Ask HN: Who is using MCP in production?
fny·Mar 1, 2026
I hate MCP servers That said the core argument for MCP servers is providing an LLM a guard-railed API around some enterprise service. A gmail integration is a great example. Without MCP, you need a VM as scratch space, some way to refresh OAuth, and some way to prevent your LLM from doing insane things like deleting half of your emails. An MCP server built by trusted providers solves all of these problems. But that's not what happened. Developers and Anthropic got coked up about the whole thing and extended the concept to nuts and bolts. I always found the example servers useless and hilarious.[0] Unbelievably, they're still maintained. [0]: https://github.com/modelcontextprotocol/servers/tree/main/sr...
In When does MCP make sense vs CLI?
bgauryy·Nov 3, 2025
I built Octocode MCP, an MCP server that enables advanced research on GitHub for any purpose: searching code, exploring repository structures, reading targeted file ranges, analyzing PRs, and synthesizing findings to support code generation, complex flow understanding, and feature exploration (for both public and private organizations). Motivation: To fill knowledge gaps in large organizations and the open-source world, and to create smart context for AI agents tackling any task! Highlights - Understand unfamiliar code and features quickly: follow a feature from API to UI—even across repositories and services. - Answer concrete questions with code evidence, not just documentation: "Where is X done?" "How does Y work?"—always with cited references. - Close the context loop for PMs, developers, analysts, and managers with shared, traceable
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findings. - Research like a senior engineer: discover → verify → explain. Designed by an architect who used to do this manually in production and in code for many years. - Works with your existing GitHub access: supports public and private organization repositories via GitHub CLI or PAT. - Efficient and secure by default: targeted reads, token‑efficient summaries, and secret‑aware sanitization. Currently, it is used by several organizations, has ~4.5k downloads per week (according to npm), and is an official Model Context Protocol community MCP server - https://github.com/modelcontextprotocol/servers Today, it helps developers, product managers, analysts, and engineering managers better understand code and features, closing the context and knowledge loop in large organizations—and even for indie developers. I'm looking for feedback from real-world use cases, reports of rough edges, ideas for additional research workflows, and further collaboration or sponsorship to take this project to the next level (this is just an initial phase, and there are many plans for the future). Links - GitHub: https://github.com/bgauryy/octocode-mcp - NPM: https://www.npmjs.com/package/octocode-mcp Quick start 1. Install and authenticate to the GitHub CLI – https://cli.github.com/ 2. Add Octocode MCP via your client: `npx octocode-mcp@latest` 3. Start asking anything about any code!
In Octocode MCP – AI Researcher for Smart, Deep Multi-Repo Code Context
bbor·Oct 18, 2025Recommendation phrase
Quite telling -- thanks for the insightful comment as always, Simon. Didn't know that, even though I've been discussing this on and off all day on Reddit. He's a smart man with well-reasoned arguments, but I think he's also a bit poisoned by working at such a huge org, with all the constraints that comes with. Like, this: You can’t just tell them something and they’ll remember it. It might take a decade to work through this issue if you just want to put a single LLM in a single computer and have it be a fully-fledged human, sure. And since he works at a company making some of the most advanced LLMs in the world, that perspective makes sense! But of course that's not how it's actually going to be (/already is). LLMs are a necessary part of AGI(/"agents") due to their ability to avoid the Frame Problem[1], but they're far from the only n
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eeded thing. We're pretty dang good at "remembering things" with computers already, and connecting that with LLM ensembles isn't going to take anywhere close to 10 years. Arguably, we're already doing it pretty darn well in unified systems[2]... If anyone's unfamiliar and finds my comment interesting, I highly recommend Minsky's work on the Society of Mind, which handled this topic definitively over 20 years ago. Namely; A short summary of "Connectionism and Society of Mind" for laypeople at DARPA: https://apps.dtic.mil/sti/tr/pdf/ADA200313.pdf A description of the book itself, available via Amazon in 48h or via PDF: https://en.wikipedia.org/wiki/Society_of_Mind By far my favorite paper on the topic of connectionist+symbolist syncreticism, though a tad long: https://www.mit.edu/~dxh/marvin/web.media.mit.edu/~minsky/pa... [1] https://plato.stanford.edu/entries/frame-problem/ [2] https://github.com/modelcontextprotocol/servers/tree/main/sr...
In Andrej Karpathy – It will take a decade to work through the issues with agents
srijanshukla18·Jul 17, 2025Submission

Show HN: Claude Memory Visualizer

I had been using Knowledge Graph Memory Server.(https://github.com/modelcontextprotocol/servers/tree/main/sr...) And ofcourse, I wanted to see the memory visually. Works for any Claude user's memory.json with zero config. Shows entities and relationships as an interactive graph with physics simulation. You can filter by type, search observations, and click nodes for details. Works with the standard Claude Memory MCP format (NDJSON). GitHub: (https://github.com/srijanshukla18/claude-memory-viz) The visualization in the README is from my actual Claude memory - 72 entities, 93 relationships. Pretty cool to see how the AI organizes knowledge. This is 100% vibe coded. Also, my friends are using it to try and build something cool that gets on the same idea of wanting to see your stuff visually, get on the list if you are interested - https://ta
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lly.so/r/w7Ork9
3 points · Open discussion
iosifache·Jul 9, 2025
It's doable, as you'll also find MCP servers for reading files [1]. Claude Desktop also has a built-in file reader [2], so you can ask it to read the file and process the content (e.g., generate a summary or even a meta-summary [3]). [1] https://github.com/sylphxltd/pdf-reader-mcp [2] https://github.com/modelcontextprotocol/servers/tree/main/sr... [3] https://x.com/iosifache/status/1942247320302547175
In Show HN: MCP server for searching and downloading documents from Anna's Archive
asteroidburger·Jun 29, 2025
MCP itself doesn't require the use of the LLM. There are other concepts, but for this use, Tools are key. A Tool is an operation, like a search. Have a look at the Filesystem example MCP server - https://github.com/modelcontextprotocol/servers/blob/main/sr.... It has a collection of Tools - read_file, read_multiple_files, write_file, etc. The LLM uses MCP to learn what tools the server makes available; the LLM decides when to use them. (The process is a little more complicated than that, but if you're just trying to call tools without an LLM, those parts aren't really important.) If you ask the LLM, "Find all files with an asterisk," it might deduce that it can use the search_files tool provided by that MCP to gain the needed context, invoke that tool, then process the results it returns. As an engineer, you can just call search_files if
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you know that's what you want.
In MCP: An (Accidentally) Universal Plugin System
ashwinsundar·Jun 24, 2025
You don't need to aspire to use "MCP", but rather select some MCP servers that will be useful for you. MCP servers basically expose pieces of the web/data that your LLM client has not been trained on. Maybe some private APIs, or internal data that isn't part of training data sets. Real-time data is another good use case. This list has a lot of ideas: https://github.com/modelcontextprotocol/servers?tab=readme-o... I personally use the Kagi MCP server (allows an LLM client to execute searches against the Kagi search engine), and stochastic thinking/sequential thinking (some alternate thought patterns that help models escape the "valley of optimization" problem). You can't tell Claude.md to "use the Kagi search engine"...it doesn't have an API key to do that. The MCP server basically just exposes the API. You're right, it's a lot less exciti
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ng than people make it sound. The future possibilities are where the excitement is at imo. I'm excited about MCP more from a developer side, than from a user side. It really does feel like the first week of mobile apps still. Will it "revolutionize everything"? No, just like mobile apps didn't solve every problem (and made some worse).
In MCP is eating the world

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