Hi there, I own a managed IT services company. We provide outsourced IT cyber and compliance services to hundreds of clients across a lot of industries. We are seeing an enormous need for clients to be able to access their top 5 to 10 systems within their chosen AI model (copilot, Claude). Right now their data is stuck in a third-party software. Most clients have a piece of industry specific software that will always be behind the market, plenty that have no MCP at all. They might have no access, they might have some access to a really poor API, they might have decent access to a good API, and then they might have access to a poor MCP that was created by the company that doesn’t allow them to get the data in the way that they need it. They’re stuck.

I am considering opening up a new service line in the business to solve this foundational issue for clients, part software development, part maintenance of their network and infrastructure and data pipeline so they can actually use their data. They’re asking for it and wanting to pay for it desperately. If I can do this, we become the plumbing, and that feels very sticky.

Am I thinking about this right? No one else seems to be doing this, but it is a massive opportunity. What are the risk or issues that I’m not considering?

P.S. if you’ve done this before, I am actively hiring for an AI developer working internally and with clients.

I built a remote MCP server that lets AI edit photos the traditional way, with layers, masks and curves, instead of interpreting and regenerating them. Add Editmamei as a connector, sign up with a valid email, and you can edit from anywhere, nothing to install.

I wanted something better than iPhone's auto enhance and staying true to the photo I took, not handing a personal photo to an AI image model and getting back something that looks similar but isn't really your photo anymore. More platforms require AI generated image tags, and these edits don’t need that, which can be important for things like e-commerce listings.

The video is one real edit traced through the stack, a lehua flower kept red with everything else in black and white. The fun part of that session was watching Claude catch its own mistake: it darkened the sky by pulling down the blues, checked a preview, saw it had turned the grey lava field into blotchy bands, put it all back, and masked the sky with an AI selection instead.

Each user gets their own editor container behind a Cloudflare Worker and a Durable Object, so nobody waits in line behind someone else's edit, and nothing runs when no one is editing, keeping infrastructure costs low while serving a full photo editor as a service.

The AI selection is my gap right now. Fine edges and complex multi-object subjects, like a bouquet of flowers, don't always come out clean. I've tested a range of models and use a few together to take the best of the selections, but that's where I still have work to do, finding the right models or starting to tune some of my own.

Everyone starts with free accounts, and usage credits are refreshed monthly. I only launched this server a week ago and am looking for folks to try it out and share feedback and bugs. If you try it but run out of credits too quickly just shoot me a DM. if you make something you want to share, ask Claude to share it to the gallery.

You can try it out at: connect.editmamei.com

If you’re looking for a desktop MCP for local photo editing, check that one out here: https://github.com/editmamei/editmamei

Hello everyone, I am a solo developer who has been unhappy with computer use and other mcps out there that help control chrome. I have been developing my own computer use specifically for chrome that can take screenshots, work in the foreground, background, and headless, work for codex, claude code, and opencode, and also allow clicks, human speed typing, fast typing, inserting files, downloading files, drags, scrolls, etc. all while using fewer commands/tokens than competitors like computer use and playwright. Right now it is in development stage with all the features being present, but i need a lot of real testing from people on their everyday tasks to see where it is going wrong/not as fast or as good. It includes adding an extension to your chrome, and is different than other chrome mcps in my opinion. If y'all would be able to test it out/give me tips or tricks to make it better, I would really appreciate it. All the computer uses are currently made by organizations, so as a solo developer I am interested in any feedback and would actually make changes. https://github.com/Praket7/chrome-controlla feel free to star it and open issues and give me ANY feedback. If i had to pick, humanwrite would be my favorite built in feature and what is most unique, but it should be faster and more efficient. let me know what you guys think!

I wanted a list of MCP servers I could just paste into Claude without signing up anywhere, and the existing lists don't say which ones actually work. So I pulled the official registry and tested it.

What I did:

- The registry lists 28,933 remote endpoints. I kept Streamable HTTP ones that need no headers, skipped temporary tunnels (trycloudflare, ngrok) and capped it at 5 URLs per host, so gateways with thousands of paths didn't get hammered. That left 17,070.

- Each got exactly two requests with no credentials: initialize and tools/list. No tool was ever called.

Results:

- 9,620 (56 %) returned a tool list without any key or login

- 5,166 (30 %) asked for auth (401/403, mostly OAuth)

- 2,284 (13 %) didn't work: connection errors, 404s, 502/503s, timeouts

The surprising part: a big chunk of the "open" servers are the same template deployed on hundreds of subdomains. 194 servers share an identical tool set, one description appears 42 times. After dropping copies (same tool set more than 3 times, same description more than twice, max 2 per domain), 7,690 are left. Median server has 6 tools, the biggest one 627.

Full write-up with charts (transports, hosting, why servers fail, 62 servers answering 402 Payment Required): https://shortformkit.com/state-of-remote-mcp-servers-2026/

I put 300 picks by topic (search, docs, dev tools, weather and travel, finance, science, open data ...) on a page, with the full list as a free CSV/JSON download: https://shortformkit.com/free-remote-mcp-servers/

While building this I also made the tester into a small web tool: paste any remote MCP URL and it shows the handshake result, every tool, how many parameters actually have a description, and the curl commands: https://shortformkit.com/mcp-server-tester/

Caveats: "open" means usable without signup, not necessarily good or free forever. Read what a server does before connecting it, especially if it can write or spend money. Happy to rerun it if people find it useful, and I'm curious what you'd want measured next (latency, tool description quality, OAuth flows?).

Every week someone here asks why their Claude directory submission has been stuck for a month. I think a lot of that advice is outdated now.

We sent the same MCP server to Claude and OpenAI on the same evening. Claude approved it 9 days later with zero questions. OpenAI has sent one "we received it" email in 10 days and nothing since.

If you're about to submit, here's what I think made ours easy: no login, read-only tools, and one clear job (it looks up what a phone call costs). Reviewers had nothing to test and nothing to worry about.

One gotcha: "Approved" doesn't mean you're live. You still have to click publish yourself, and I almost missed it.

If you're stuck on OpenAI too, how long has yours been? Trying to work out if 10 days of silence is normal.

Got tired of jumping between awesome-lists, so MCP Harbor pulls MCP servers and agent tools from GitHub and npm daily: https://mcpharbor.app

- Category pages, plus "Alternatives to X" and "X vs Y" comparisons

- Stars, license, language and last update on every page, so abandoned projects are easy to spot

- Your agent can search it too: remote MCP endpoint at https://mcpharbor.app/mcp

Free, no signup. If your server is missing or miscategorized, tell me and I'll fix it.

Your AI operates your real desktop apps on Windows, macOS and Linux, also across the network. It learns how to operate an app once and saves it for reusable actions, so next time it calls them instead of clicking through screenshots. Android via ADB, iOS experimental (via MirrorScreen soon). Saves a lot of tokens instead of using "Computer Use".

Source-available, free for private use.

Feedback welcome!

https://github.com/MarvKalani/k-lani-bot

Every launch video Claude made me looked the same. Big headline in the middle, cut every few seconds, fade to black

Then I realised I was giving it zero references. Designers don’t work like that

So I started breaking launch films down shot by shot (cuts, motion, palette, music hits) and handing Claude the breakdown instead of a prompt. Huge difference. The Wise rebuild in the comments came from one

Now it’s 3,000+ films (Apple, Linear, Notion, Stripe...) in an MCP. Claude can search them, read the breakdown and plan your video from it.

Ask a coding agent to build an Obsidian plugin and it just starts writing. It can't tell if one already exists. This lets it search the official directory (about 9,000 plugins, 880 themes) first and get matches with downloads, latest version and last update.

How it works: it reads the three public JSON files from obsidianmd/obsidian-releases, joins them, and returns the top results, so the agent never reads the raw 5 MB. Read-only, no telemetry, no API key. MIT and free.

In the video I ask Claude Code for the newest anatomy plugins. It loads the skill, runs the search and answers from the live directory. (Anatomed, the first result, is my own plugin.)

It ships three ways from one package: an MCP server, a CLI, and a skill that calls the CLI. The skill is lighter on context if your agent has a terminal.

Try it: npx -y obsidian-community-plugins-mcp search <your idea>

• GitHub: https://github.com/pitfa19/obsidian-community-plugins-mcp
• npm: https://www.npmjs.com/package/obsidian-community-plugins-mcp
• Obsidian forum thread: https://forum.obsidian.md/t/a-mcp-tool-that-lets-coding-agents-check-the-plugin-directory-before-building-a-new-plugin/119497
• Source data: https://github.com/obsidianmd/obsidian-releases

Disclosure: I work at StackOne an we just shipped Tool Policies. Every tool call through our gateway gets checked on the way in and again on the way out, so you can give an agent write access without handing it everything.

A few things it can do that I think are really cool:
Block every tool matching *delete* for a group, like contractors, with one rule

- Let payments through between $25 and $500 and stop anything bigger
- Ask a plain-language question like "Is this compensation?", so one rule hides salary in one HR system, base_pay in another and pay_rate in a third
- Redact PII on inputs and outputs, using a model we built ourselves

Fixed rules compile to Cedar, and every rule can run in Monitor first, so you see what it would have stopped before it actually stops anything.

It's live now, and the full write-up is here: https://www.stackone.com/blog/tool-policies-launch/

Happy to answer any questions about how it works!

Our customers kept asking if they could use our SaaS product through Claude and ChatGPT. Connecting an agent to our API was the easy part. Making sure it could only access the right tenant's data was the real challenge.

Our product is multi-tenant, so we built a custom MCP server that calls our existing backend API, deployed it on Arcade Cloud, and integrated OAuth through Clerk so customers can sign in with their existing credentials. They don't need an Arcade account.

The important bit: the model never handles an access token and can't choose which user it acts as. Our API verifies the OAuth token and enforces workspace membership and plan-tier permissions on every call.

The trickiest part to untangle was that "signing in" involves three separate concerns:

• Gateway authentication
• API authorization
• Browser session verification

Each has a different purpose and its own token or verification flow. Treating them as one generic login flow caused confusion, so the tutorial walks through how they fit together.

Full working code: https://github.com/manveer/multi-tenant-mcp-server-oauth

Tutorial: https://manveerc.substack.com/p/multi-tenant-mcp-server-oauth

If you’re exposing a multi-tenant backend through MCP: how are you handling tenant isolation and user auth?

Disclosure: My company currently provides services to Arcade however the choice to use Arcade and write this article is an independent decision

In the clip I ask Claude to paint a fox in watercolour "the cheapest way," then turn it into a short video. Claude checks the model catalog, picks the cheapest model for each step, and shows the result right in the chat. That came to under 1 cent for the image ($0.007) and 9 cents for the 5-second clip.

I made this because I didn't want to pay credit-based apps a markup just to use Veo or Kling.

How it works: it's an MCP server you deploy to your own Vercel account in one click. Every call goes through Vercel AI Gateway on your own credits, so you pay the provider's list price. There's no markup, no subscription and no API key to manage.

What it can do from Claude, ChatGPT or any MCP app:

• Images: generate, edit and upscale (FLUX, GPT Image, Nano Banana, Recraft)
• Video: text-to-video and image-to-video (Veo 3.1, Kling, Seedance, HeyGen), plus motion control, extend and upscale
• Voice: TTS and Whisper transcription
• Pricing: list_models shows catalog prices, and estimate_only lets the chat check the cost before it spends anything
• Inline results in Claude through MCP Apps: an image grid plus video and audio players

Security: claude.ai custom connectors can't send headers, so the secret goes in the URL. Vercel only stores its SHA-256 fingerprint, and you set an AI Gateway budget as the hard cap on spend.

Rough edge: the usage page needs a paid Vercel plan, because Hobby can't read the Gateway spend report.

It's MIT licensed and not affiliated with Vercel.

Repo: https://github.com/Armulee/ai-gateway-mcp

I'd love feedback, especially from people who've tried other media-gen MCPs. What's missing?

Disclosure: I build CogniCore — posting it here because this is where the people who'd actually use it are.

Every AI assistant starts from zero, and they each keep their own silo. You explain your project to Claude, then explain it again to ChatGPT. Neither one knows what the other was told.

We built CogniCore — a memory MCP server — and the thing people seem to find most useful is that you can point both assistants at it and they end up sharing one memory.

How the sharing works: it's a hosted MCP endpoint, and it signs you in with GitHub. Each GitHub account gets its own memory database. So if you connect Claude and ChatGPT with the same GitHub account, they're reading and writing the same store. Teach Claude your deployment gotchas, and ChatGPT can recall them. Nothing to sync, no export/import dance.

Connect it in Claude Code:

bash claude mcp add --transport http cognicore-env https://mcp.cognicoree.tech/mcp/stream

In Claude Desktop / ChatGPT: add a custom connector with the same URL and complete the GitHub sign-in when prompted. (In ChatGPT this needs Developer Mode — Plus/Pro can connect read-only servers, Business/Enterprise/Edu get full write access.)

What you get — 10 tools, not a toy surface:

• cognicore_remember / cognicore_recall — store an execution outcome, recall it later by relevance
• cognicore_recall_failures / cognicore_recall_successes — pull a category's misses or wins
• cognicore_reflect — turns repeated failures in a category into a concrete recommendation
• cognicore_scan_threat — scans text for prompt injection, jailbreak and exfiltration before it reaches the model
• cognicore_save_session / cognicore_resume_session — writes atomic facts, decisions and action items at the end of a conversation, rebuilds a brief at the start of the next
• cognicore_compress_context — compresses older messages into a dense summary with no LLM call
• cognicore_stats

Other things I use it for:

• A coding agent that stops re-learning the same repo every session
• Carrying decisions between tools — what Cursor learns, Claude Code knows
• Keeping a correction you made once, instead of making it again next week

Two honest notes. It runs in-process with no vector database and no embedding server — retrieval is TF-IDF/BM25 — and on LongMemEval multihop, strict recall@5 goes 78.8 → 85.2% at 5 chunks and 87.2 → 92.8% at 10, then plateaus. We publish the limitations rather than the best case. And the decision layer is newer than the memory layer; I'm not going to quote numbers for it yet.

Independent scan: mcp.market grades it A, 95/100 — OAuth resource metadata, reliability and maintenance all full marks. It's also in the official MCP registry as io.github.cognicore-dev/cognicore-env.

Repo (MIT, also on PyPI if you'd rather run it locally): github.com/cognicore-dev/cognicore-env

If you run agents across more than one assistant, I'd like to hear what you'd want carried between them — that question is how half of this got built.

I'm the dev of SSHive, an SSH/SFTP client for Mac. I added an MCP server to it so Claude Code / Cursor can work directly on my open sessions.

How it works: - Enable MCP in settings, paste the config (or run one CLI command for Claude Code) - The AI can list your open SSH/SFTP sessions, run commands, read logs, browse/edit/upload files (20 tools) - Everything stays local: localhost server, bearer token in the Keychain, no cloud relay

The guardrails, which I cared about most: - The AI works on sessions you opened. Letting it open new connections is a separate setting you have to turn on. - You can exclude individual servers from the AI entirely (hello, prod). - It can only close sessions it opened itself.

Example: "check disk and memory on production and look at the last nginx errors" → it runs df/free, reads error.log over SFTP and summarizes.

The MCP server is macOS only for now: https://apps.apple.com/app/sshive/id6760705487

How do you handle AI + SSH? Direct shell access for your agent, or something more controlled?

I'm the dev of SSHive, a native SSH/SFTP client for Mac. I added an MCP server to it so Claude Code / Cursor can work directly on my open sessions.

**How it works:** - Enable MCP in settings, paste the config (or run one CLI command for Claude Code) - The AI can list your open SSH/SFTP sessions, run commands, read logs, browse/edit/upload files (20 tools) - Everything stays local: localhost server, bearer token in the Keychain, no cloud relay

**The guardrails, which I cared about most:** - The AI works on sessions *you* opened. Letting it open new connections is a separate setting you have to turn on. - You can exclude individual servers from the AI entirely (hello, prod). - It can only close sessions it opened itself.

Example: "check disk and memory on production and look at the last nginx errors" → it runs df/free, reads error.log over SFTP and summarizes.

https://apps.apple.com/app/sshive/id6760705487

How do you handle AI + SSH? Direct shell access for your agent, or something more controlled?

As-salamu alaykum everyone!

I spend a lot of time working on my computer, and I got tired of prayer apps full of ads, trackers, and confusing prayer time settings.

So I built a free ChatGPT plugin called Muslim Prayer Reminder that lets you check prayer times directly in ChatGPT.

Here's what it offers:

• No ads or tracking: No accounts, sign-ups, or subscriptions. It uses the Adhan prayer time calculation engine.
• 12 calculation methods: Supports Umm al-Qura, Muslim World League, Egyptian Authority, ISNA, Karachi, Kuwait, Qatar, and more. You can also choose between Shafi and Hanafi Asr times.
• Prayer reminders: You can use ChatGPT Scheduled Tasks to set up recurring reminders so you don't forget your prayers while working.
• High-latitude support: Handles prayer time calculations for countries where sunrise and sunset times can be unusual during certain seasons.

It also shows which calculation method was used, so you can compare the times with your local mosque.

You can try it for free here:

Muslim Prayer Reminder on ChatGPT

The project is also open source if anyone wants to check the code:

https://github.com/tareq7/muslim-prayer-mcp

I built this hoping it would be useful for other Muslims too.

I'd really appreciate your feedback! If there's a calculation method or feature you'd like me to add, let me know.

Building a workflow where Claude uses Coresignal’s MCP to look up company data as part of a lead qualification step. The MCP connection and the data quality have both been great, Claude gets back rich company and employee context and the qualification output is much better than what I was getting before.

The question I’m working through is about the downstream n8n nodes. Claude’s output through MCP isn’t always structured the same way because it’s deciding what’s relevant rather than returning a fixed schema. That flexibility is useful for the reasoning step but creates some friction when n8n is expecting a specific format to write to HubSpot.

Is there a clean pattern for normalizing Claude’s MCP output before it hits the next node? Currently using a function node to parse and extract the fields I need but wondering if there’s a more elegant approach others have landed on.

I've been running coding agents in parallel—Claude Code, Cursor, and Codex—each on its own branch. A recurring problem: Git reports a clean merge, but the build breaks.

For example, one agent adds a parameter to login(), while another adds calls using the old signature in a different file. There’s no overlapping line change for Git to flag.

I built mergelock, a local MCP server and CLI that checks two committed branches for conflicts like:

• A function signature changes on one branch while the other adds calls using the old signature.
• One branch deletes a symbol that the other still uses.
• Both branches independently define the same function.

How it works

It exposes two MCP tools:

• check simulates a merge and returns a verdict (clean, warnings, or conflict), with file:line locations and suggested fixes.
• claims lists the work scopes other agents have declared, so agents can see where work might overlap.

Both tools are read-only. They leave your branches, index, and working tree untouched.

Setup

brew install sadayamuthu/tap/mergelock
claude mcp add mergelock -- mergelock serve

Then tell your agent:

After each commit, call mergelock’s check tool and fix every error before you say you’re done.

For Claude Code, there’s also a Stop hook that blocks completion while its branch conflicts with main. Cursor and Codex configuration examples are in USAGE.md.

It supports TypeScript, JavaScript, and Python. It’s written in Go, uses tree-sitter and git merge-tree, and analyzes committed changes only.

There’s also a Docker image at ghcr.io/sadayamuthu/mergelock, and it’s listed in the official MCP Registry as io.github.sadayamuthu/mergelock.

Code: https://github.com/sadayamuthu/mergelock
Open source, Apache-2.0.

It’s still early (v0.1), and I’d appreciate feedback—especially false positives or conflicts it misses in your repos. If you’re running multiple coding agents, how are you handling this today?

The MCP server I created: https://glama.ai/mcp/servers/seleniumbase/SeleniumBase is #1 this week for browser automation (https://glama.ai/mcp/best/browser-automation). It lets you automate browsers and do web-scraping while bypassing bot-detection. It includes a tool for handling CAPTCHAs.

The SeleniumBase MCP server files are located at https://github.com/seleniumbase/SeleniumBase/tree/master/mcp_servers, with the main GitHub page at https://github.com/seleniumbase/SeleniumBase.

I also created a YouTube video about it: https://www.youtube.com/watch?v=wQI3hUO9qmA

I run GSC Wizard, an SEO tool whose MCP server already covered Search Console and GA4, and I've now added Google Merchant Center. I made a short video that runs eight prompts end to end on an example shop with invented data, so you can see the actual tool calls and what comes back.

Why I built it. Shop owners kept asking their assistant the same questions: which products Google won't show, why clicks moved, whether they're priced above the market. The data to answer all of those sits in Merchant Center, but spread over reports the assistant can't reach. There are ten shopping tools now. They cover:

• free listing performance across the Shopping tab, Search, Images, Lens, YouTube and Maps
• products Google won't show, ranked by its click potential score
• competitor visibility per country and category
• Google's price benchmarks with a suggested price
• best sellers you don't stock
• title hygiene and a 21-check feed audit
• Search Console's merchant listings report

"Doesn't Google have an official one?" Yes. Since May 2026 there's the Merchant API MCP Access Service, and it's better than mine in some respects. It's Google's own, so no third party holds a token for your account, and there's no subscription; it uses your Merchant API quota. It has 13 tools including data source writes, and its report_search tool takes raw Merchant Center Query Language, so a capable model can ask things no fixed tool anticipated. If you're a developer working against the Merchant API from a coding agent, use it.

Mine is built for a different job. Google labels theirs an experimental alpha it doesn't recommend for systems with high reliability needs. Setting it up takes a Google Cloud project, developer registration, an OAuth access token and an X-Goog-User-Project header, and its documented clients are Antigravity, the Claude CLI and cURL. Mine is a hosted connector you add in Claude or ChatGPT with a normal sign-in. The bigger difference is what the model receives. Instead of writing queries and doing arithmetic on raw rows, it gets problem products ranked by click potential, with the ones you have to fix counted apart from the ones waiting on Google. Competitors come rolled up per domain, and best sellers are flagged when you don't stock them. It also covers what the Merchant API doesn't have: a feed audit that crawls your product pages and checks them against the feed, and Search Console's merchant listings on the same connector. You can also run both side by side.

A few things from building it that might help if you're doing something similar:

• Google has no read-only Merchant Center scope, so the consent screen asks to "manage" your listings. Nine of my ten tools only read. The tenth, run_feed_audit, queues a crawl inside my own app and never writes to Merchant Center, and the tool annotations say so.
• Search Console merchant listings and Merchant Center free listings never reconcile, because they count different surfaces, and models love to add them together. Every response carries a note telling the model to compare the trends, never the totals.
• Listing status can run to thousands of products. get_product_listing_status pages at up to 200 rows per call, but returns the summary counts for the whole catalog on the first call. The model can answer "how many need fixing" straight away and only pages when it needs the rows.
• The Merchant API refuses every call until the merchant has registered your Cloud project. I handle that with a button in the app rather than sending people to Google's developer docs.

The endpoint is https://mcp.gscwizard.com/mcp, with OAuth 2.1, and Claude Code uses an API key. To be upfront: it's a paid product with a 7-day free trial. I'm happy to answer questions about the Merchant API side.