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Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.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
Vectorsearch
Overview
Description from the maker (original English)
Store and retrieve text in a Qdrant vector database by similarity search.
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 on your computer
- Transport: stdio
- 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 your computer with your user’s permissions (files, network, apps).
- The registry lists no key to fill in.
You need
- npmNode.js installed (for the npx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"vectorsearch": {
"command": "npx",
"args": [
"-y",
"@reneturcios/vectorsearch",
"mcp"
]
}
}
}Run this in your terminal
claude mcp add vectorsearch -- npx -y @reneturcios/vectorsearch mcpOr copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"vectorsearch": {
"command": "npx",
"args": [
"-y",
"@reneturcios/vectorsearch",
"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.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"vectorsearch": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@reneturcios/vectorsearch",
"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.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
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
Runs on your computer; the tool list appears in your AI app after you install it.
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 with the GitHub account CloudCorpRecords, which owns the repo
- … 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 applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Search & AI memoryi
- More stars than 0% of the other 7226 servers in this category
- Published to the registry more recently than 85% of the other 7226 servers in this category
- Basic checks equal to or better than 100% of the other 7226 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 Vectorsearch 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 on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (0.1.0) reached the registry 6 days ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js, Docker on your computer.
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
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.
vectorsearch
vectorsearch is a small memory for agents: store a piece of information now, then ask for it back later with a plain-language query. It keeps exactly two operations — store and find — and backs them with a Qdrant vector database over its REST API.
Set QDRANT_URL to reach a non-default service (it assumes http://localhost:6333). The collection used when a call does not name one comes from COLLECTION_NAME and defaults to documents. Storing into a collection that does not exist yet creates it first, sized to this tool's embedding dimension with cosine distance.
Install
npx @reneturcios/vectorsearch find "how do we deploy the staging app"Or run the container image:
docker run -i ghcr.io/cloudcorprecords/vectorsearchThe container defaults to the MCP server (see below). Pass CLI arguments to use it as a CLI instead.
CLI
# Store something (optionally into a named collection)vectorsearch store "the staging deploy runs from the release branch" --collection notes
Ask for it back
vectorsearch find "staging deploy" --collection notes
Start the MCP server
vectorsearch mcp
vectorsearch --version
Exit codes: 0 on success, 1 when the service call fails (the error includes the HTTP status), 2 for usage errors.
MCP server
One bin serves both faces. The mcp subcommand starts a stdio MCP server:
npx -y @reneturcios/vectorsearch mcpMCP client configuration:
{"mcpServers": { "vectorsearch": { "command": "npx", "args": ["-y", "@reneturcios/vectorsearch", "mcp"], "env": { "QDRANT_URL": "http://localhost:6333", "COLLECTION_NAME": "documents" } } } }
Tools
| Tool | Required | Optional | Purpose |
|---|---|---|---|
qdrant-store | information (string) | metadata (object), collection_name (string) | Store some information in the Qdrant database. The text and any metadata become the point payload. |
qdrant-find | query (string) | collection_name (string) | Retrieve relevant information from the Qdrant database. Returns the closest stored entries with their payloads and scores. |
About the embeddings
The reference design pairs Qdrant with an external embedding model. This rebuild deliberately calls no external embedding service. Instead it embeds text locally with a deterministic hash-based function: the same text always produces the same unit vector, and texts that share tokens land near each other, which keeps store and find self-consistent and fully offline. Be clear-eyed about the trade-off: this is a local fallback with token-overlap similarity, not model-grade semantic embeddings. Swapping in a real embedding provider later only changes the executor's vector function — the tools, schemas, and storage shape stay the same.
Point ids are likewise deterministic, derived from a hash of the text (and metadata when present), so re-storing the same information updates the same point instead of piling up duplicates.
Development
npm installnpm run build # tsc -> dist/ npm test # node test runner over test/*.test.ts, fakes only, no live service npm run dev # run the CLI from source via tsx
Dispatch, default-collection resolution, and error propagation are tested against a fake executor; the Qdrant REST executor is tested against a fake fetch that records its requests (collection ensure / create-on-missing, point upsert shape, search mapping).
License
MIT — see LICENSE.