MCP Junction

Name: MCP Junction
Author: rickstek

by rickstekversion 1.0.0i★ –iRuns in the cloud

Registry nameai.mcpjunction/directory

Install

Overview

Description from the maker (original English)

Search a nightly-refreshed directory of MCP servers by keyword, category or topic.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs in the cloud
Transport: Streamable HTTP
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on the maker’s server: what you ask it goes through their server.
The registry lists no key to fill in.

You need

Runs in the cloudNothing to install; it connects over the internet

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Works in Claude on the web, desktop and mobile:

Open Settings → Connectors
Press Add custom connector
Paste the address below and press Add
If it asks you to sign in, sign in with the maker

https://mcpjunction.ai/mcp

Run this in your terminal

claude mcp add --transport http directory https://mcpjunction.ai/mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "directory": {
      "url": "https://mcpjunction.ai/mcp"
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "directory": {
      "type": "http",
      "url": "https://mcpjunction.ai/mcp"
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

ChatGPT connects only to servers on the internet:

Open ChatGPT on the web → Settings → Connectors (Apps)
Press Create or Add (some plans need Developer mode under Advanced first)
Paste the address below, give it a name and save
If it asks you to sign in, sign in with the maker

https://mcpjunction.ai/mcp

Menu names can differ slightly by plan and over time.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

We have not read this server’s tool list yet; we check endpoints in rotation.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified against the domain mcpjunction.ai (the registry checks domain ownership)
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
… Not checked yet
Endpoint responds
Not checked yet; we check endpoints in rotation
… Not checked yet
Sign-in with OAuth
Not checked yet; we check endpoints in rotation
… Not checked yet
No hidden instructions in tool descriptions
Not checked yet; we read the tool list when we check the endpoint

Compared with others in Search & AI memoryi

More stars than 0% of the other 7236 servers in this category
Published to the registry more recently than 35% of the other 7236 servers in this category
Basic checks equal to or better than 100% of the other 7236 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is MCP Junction safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs in the maker’s cloud. Connect over the internet with nothing to install, but what you send passes through the maker’s servers.

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.0.0) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT in developer mode.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated 3 days ago

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 44 days ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

mcpjunction.ai

A directory of Model Context Protocol servers, rebuilt nightly from public GitHub metadata. Roughly 1,800 servers in a hand-owned category taxonomy, also browsable by repository topic, published as a website, a downloadable dataset, and an MCP endpoint your agent can query directly.

mcpjunction.ai · Dataset · Categories · Licensing

Not affiliated with Anthropic or the Model Context Protocol project.

This repository is the whole thing: the pipeline that builds the dataset, the Astro site that renders it, and the Cloudflare Worker that serves it.

Use the data

Three ways in, all public and all free to read.

Bulk download. The full directory, regenerated nightly with a generated_at timestamp.

curl -O https://mcpjunction.ai/data/mcp_servers.json

mcp_servers.json — ~2.3 MB, 28 fields per server plus a dataset envelope with counts and per-category totals
mcp_servers.csv — the same data as 16 flat columns

Query it from an agent. /mcp is itself an MCP server — streamable HTTP, stateless JSON-RPC over POST, protocol version 2025-06-18, no authentication.

claude mcp add --transport http mcpjunction https://mcpjunction.ai/mcp

Tool	What it does
`search_servers`	Free-text search, optionally filtered by `category` and `language`
`get_server`	One server by id (`owner--repo`, e.g. `microsoft--playwright-mcp`)
`list_categories`	Every category with active counts
`get_dataset_info`	Counts, `generated_at`, source, and licensing terms

GET /mcp returns 405 by design; POST your JSON-RPC messages. JSON-RPC batching is not supported — it was removed in protocol 2025-06-18.

Read it as markdown. Every server and category page has a clean markdown twin with no navigation or styling to strip. Either negotiate for it:

curl -H 'Accept: text/markdown' https://mcpjunction.ai/servers/microsoft--playwright-mcp

…or just append .md to the canonical URL — /servers/microsoft--playwright-mcp.md, /categories/databases.md. Each HTML page advertises its twin via .

Machine-readable entry points are collected in llms.txt.

How a server gets listed

Listing is automatic. There is no submission form, and none is planned — the pipeline runs five GitHub search queries (topic:mcp-server, topic:model-context-protocol, topic:mcp, and two name/description phrase searches), then keeps repositories that clear a 2-star floor and match a Model Context Protocol name/topic heuristic. If your server is on GitHub and meets that bar, it will appear on the next nightly run.

Corrections, removals, and licensing: , acknowledged within three business days.

Contributing

Open to read and reuse; deliberately narrow about what comes back.

Corrections are the most useful thing you can send. A wrong install hint, a stale license, a repository that moved, a server sitting in the wrong category — open an issue or email the address above with the server id (owner--repo) and what's wrong. One maintainer cannot check 1,800 entries by hand, and the people who hit these find them first.

Pull requests are welcome for the software — src/, worker/, scripts/, docs/. Bug fixes need no preamble. Open an issue first for anything that changes rendered output or the dataset shape, because the constraints in the next section are load-bearing and easy to undo by accident.

Three things don't take pull requests, for reasons rather than ceremony:

`public/data/` — regenerated nightly, so the edit is gone within a day. If a server is missing, the fix is upstream: tag the repository and the next run finds it.
`editorial/summaries.md` — summaries are written in-house. A directory that other people cite lives or dies on whether its prose is trustworthy, and reviewing a summary properly costs about what writing one costs.
`categories.json` and `topics.json` — every slug is a permanent public URL, and category order is matching precedence rather than presentation. Propose one in an issue, with the servers it would cover; adding a category is a decision, not a patch.

The code is MIT and contributions to it are accepted on those terms. The dataset is separately licensed — see Licensing.

How the data is kept honest

A directory other people cite has to fail safe. These are the constraints the code actually enforces:

Automation cannot promote anything. security_reviewed, verified_badge, sponsor_tier, editorial_notes, and editorial_summary are set by hand and only carried forward by the pipeline — never written by it. A machine cannot grant a badge or a security clearance here. These fields are currently unset across the entire dataset; a written summary, when one exists, is a description and not a security review.
Collapse guard. A run aborts if the active server count falls more than 10% against the previous dataset, or if more than 50 servers would be newly delisted at once. A separate check refuses to deploy below 200 servers. A bad day at the GitHub API should not silently gut the directory.
Delisted repositories persist 30 days as status: archived_or_removed with a delisted_at timestamp, then drop. Stable URLs, without unbounded growth.
Identity guard. If a repository's numeric GitHub id changes under the same owner--repo name — a rename, or a released username claimed by someone else — the editorial state is dropped rather than inherited.
CSV injection is neutralised. Cells beginning =, +, -, or @ are quote-prefixed before publication. Repository descriptions are attacker-controllable: anyone can name a GitHub repo.
Install hints never auto-confirm. No npx -y, no --yes. The registry package sharing a repository's name may belong to someone else entirely, so hints are published as unverified suggestions.
No dead links. The sitemap lists only files present on disk at build time.
One source, used as documented. The official GitHub REST API, authenticated and rate-limit aware, public repository metadata only.

How it's built

src/              Astro static site — pages, layouts, components

worker/ Cloudflare Worker — the /mcp server and markdown negotiation scripts/ pipeline.py (GitHub API → dataset), build_sitemap.py categories.json Taxonomy: human-owned, first match in file order wins topics.json Approved /topics/ pages — vetted GitHub repository topics server.json Registry metadata for registry.modelcontextprotocol.io editorial/ Human-written server summaries (summaries.md) public/ Passthrough assets — robots.txt, license.xml, llms.txt, _headers, data/ dist/ Build output and the Worker's asset root (not committed)

Every night a GitHub Action runs pipeline.py, builds the site with Astro, generates the sitemap, commits the refreshed dataset, deploys the Worker, and then verifies the live result — licensing surface, /mcp handshake, crawler access rules, and markdown negotiation — before it calls the run a success.

Locally:

npm ci && npm run dev

The pipeline needs Python 3 and a GITHUB_TOKEN; deploys need Cloudflare credentials. See docs/OPERATIONS.md.

Licensing

The code is MIT. src/, worker/, scripts/, and the configuration are yours to reuse — see LICENSE.

The dataset is not. public/data/** is excluded from the MIT grant and governed by the RSL terms in public/license.xml: search indexing is free, agent and inference-time use is free site-wide with attribution during the launch phase, and AI training requires a license. Bulk /data/ retrieval carries stricter terms. Details at mcpjunction.ai/licensing; see also NOTICE.

Citing us is easy and appreciated: *via mcpjunction.ai*.

MCP Junction

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

mcpjunction.ai

Use the data

How a server gets listed

Contributing

How the data is kept honest

How it's built

Licensing