Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Search and inspect 6,500+ curated AI apps from the HyperStore directory.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
适用于网页版、桌面版和手机版 Claude:
https://mcp.store.hypergpt.ai/mcp打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"hyperstore": {
"command": "uvx",
"args": [
"hyperstore-mcp"
]
}
}
}在终端中运行这条命令
claude mcp add --transport http hyperstore https://mcp.store.hypergpt.ai/mcpclaude mcp add hyperstore -- uvx hyperstore-mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"hyperstore": {
"url": "https://mcp.store.hypergpt.ai/mcp"
}
}
}粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"hyperstore": {
"command": "uvx",
"args": [
"hyperstore-mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"hyperstore": {
"type": "http",
"url": "https://mcp.store.hypergpt.ai/mcp"
}
}
}粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"hyperstore": {
"type": "stdio",
"command": "uvx",
"args": [
"hyperstore-mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
ChatGPT 只能连接互联网上的服务器:
https://mcp.store.hypergpt.ai/mcp菜单名称可能因套餐和版本不同而略有差异。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
我们还没有读取这个服务器的工具列表;我们会轮流检查各个端点。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 deficlow 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
尚未检查;我们会轮流检查各个端点
尚未检查;我们会轮流检查各个端点
尚未检查;我们会在检查端点时读取工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
两种方式都可以:安装在你的电脑上,或者连接云端版本。
GitHub 仓库最后更新于–,最新版本(0.1.1)于6个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接,所以接受远程 MCP 服务器的应用也可以使用,例如开启开发者模式的 ChatGPT。
同一分类中有:Context7, Codebase Memory, Exa
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
Plug 6,500+ AI apps into any LLM via the Model Context Protocol.
HyperStore is a curated directory of 6,500+ AI applications, developed by HyperGPT. This MCP server exposes the HyperStore catalog to any LLM client — Claude, ChatGPT, Cursor, Windsurf, Cline, Zed, Gemini, and anything else that speaks MCP.
Ask your LLM:
*"Find me a free AI tool that summarises PDFs."*
*"Compare ChatGPT, Claude, and Gemini side-by-side."*
*"Show me the top 5 image-generation apps with an API."*
The LLM calls HyperStore MCP behind the scenes and answers with up-to-date, curated results.
13 tools:
| Tool | Purpose |
|---|---|
search_apps | Full-text keyword search |
ai_search | Embedding-based semantic search |
get_app | Full app detail (features, screenshots, pricing) |
list_apps | Paginated apps with filters (category, pricing) |
list_categories | Browse all 30+ categories |
category_apps | Apps within a category |
browse_apps | A-Z directory listing |
get_homepage | Trending + top categories overview |
get_alternatives | Curated alternatives to an app |
list_audiences | Audience segments (developers, lawyers, …) |
apps_for_audience | Best AI tools for an audience |
list_use_cases | Use-case taxonomies (legal-contracts, …) |
apps_for_use_case | AI tools for a use case |
3 resources:
hyperstore://app/{slug} — markdown rendering of any apphyperstore://category/{slug} — top apps in a categoryhyperstore://catalog — full category index3 prompts:
find_tool_for_task — guided discovery for a taskcompare_apps — side-by-side app comparisondiscover_category — explore a topicuvx (zero install, recommended)Requires uv. One command and you're done:
uvx hyperstore-mcp
pipxpipx install hyperstore-mcp
hyperstore-mcp
docker run --rm -p 8080:8080 ghcr.io/deficlow/hyperstore-mcp
Use our managed Streamable HTTP server:
https://mcp.store.hypergpt.ai/mcp
Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": { "hyperstore": { "command": "uvx", "args": ["hyperstore-mcp"] } } }
Restart Claude → tools appear in the 🛠 menu.
claude mcp add hyperstore -- uvx hyperstore-mcp
.cursor/mcp.json (project) or ~/.cursor/mcp.json (global):
{
"mcpServers": { "hyperstore": { "command": "uvx", "args": ["hyperstore-mcp"] } } }
~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": { "hyperstore": { "command": "uvx", "args": ["hyperstore-mcp"] } } }
settings.json:
{
"cline.mcpServers": { "hyperstore": { "command": "uvx", "args": ["hyperstore-mcp"] } } }
~/.config/zed/settings.json:
{
"context_servers": { "hyperstore": { "command": { "path": "uvx", "args": ["hyperstore-mcp"] } } } }
~/.gemini/settings.json:
{
"mcpServers": { "hyperstore": { "command": "uvx", "args": ["hyperstore-mcp"] } } }
Settings → Connectors → Add custom connector:
https://mcp.store.hypergpt.ai/mcpfrom openai import OpenAI
client = OpenAI() response = client.responses.create( model="gpt-4.1", tools=[{ "type": "mcp", "server_label": "hyperstore", "server_url": "https://mcp.store.hypergpt.ai/mcp", "require_approval": "never", }], input="Find me 3 free AI tools for writing unit tests.", ) print(response.output_text)
from anthropic import Anthropic
client = Anthropic() response = client.messages.create( model="claude-opus-4-7", max_tokens=1024, mcp_servers=[{ "type": "url", "url": "https://mcp.store.hypergpt.ai/mcp", "name": "hyperstore", }], messages=[{"role": "user", "content": "Top 5 AI image generators?"}], )
See examples/ for ready-to-paste configs for every supported client.
For self-hosting, use the Docker image. For direct invocation without Docker, the CLI accepts --transport http|sse (see hyperstore-mcp --help).
When self-hosting, these environment variables can be set (see .env.example for the full list):
| Variable | Default | Purpose |
|---|---|---|
MCP_HOST | 0.0.0.0 | Bind host (http/sse transports) |
MCP_PORT | 8080 | Bind port (http/sse transports) |
LOG_LEVEL | INFO | Logging level (DEBUG, INFO, WARNING, ERROR) |
git clone https://github.com/deficlow/HyperStore-MCP
cd HyperStore-MCP uv sync --all-extras uv run pytest uv run hyperstore-mcp # stdio mode for local testing
Inspect the running server with the official MCP Inspector:
npx @modelcontextprotocol/inspector uvx hyperstore-mcp
HyperStore MCP is a thin async wrapper around the HyperStore public REST API. It is read-only — no credentials, no writes, no PII. The same data that powers the website powers the MCP server. Updates land in your LLM the moment they land on the site.
LLM client ──MCP──▶ hyperstore-mcp ──HTTPS──▶ store.hypergpt.ai/api
MIT © HyperGPT