HyperStore MCP
HyperStore MCP gives you access to a curated directory of 6,500+ AI applications, letting any MCP-compatible LLM client discover, search, browse, and compare AI tools.
Tools
search_apps— Keyword-based full-text search returning name, slug, description, pricing, and ratingai_search— Semantic/embedding-based natural language search (e.g. "something like Midjourney but free"), returning up to 12 ranked resultsget_app— Full details for a specific app by slug: long description, features, screenshots, pricing, rating, and website URLlist_apps— Paginated listing with optional filters for category, pricing model, and keyword, sorted by popularitylist_categories— All 30+ categories with app countscategory_apps— Apps within a specific category, optionally filtered by pricingbrowse_apps— Alphabetical A–Z directory browsing by starting letter (or#for digits/symbols)get_homepage— Trending apps, top categories, and catalog overview/stats
Resources
hyperstore://app/{slug}— Markdown rendering of an app's detail pagehyperstore://category/{slug}— Top apps in a given categoryhyperstore://catalog— Full category index
Prompts
find_tool_for_task— Guided discovery to find the right AI tool for a specific taskcompare_apps— Side-by-side comparison of multiple appsdiscover_category— Explore a topic or category of AI tools
Allows OpenAI models to search, browse, and get details of 6,500+ AI applications from the HyperStore catalog via the Responses API.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@HyperStore MCP Find me a free AI tool that summarises PDFs."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
HyperStore MCP
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.
What you get
13 tools:
Tool | Purpose |
| Full-text keyword search |
| Embedding-based semantic search |
| Full app detail (features, screenshots, pricing) |
| Paginated apps with filters (category, pricing) |
| Browse all 30+ categories |
| Apps within a category |
| A-Z directory listing |
| Trending + top categories overview |
| Curated alternatives to an app |
| Audience segments (developers, lawyers, …) |
| Best AI tools for an audience |
| Use-case taxonomies (legal-contracts, …) |
| 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 index
3 prompts:
find_tool_for_task— guided discovery for a taskcompare_apps— side-by-side app comparisondiscover_category— explore a topic
Related MCP server: SkillHub MCP
Install
Option A — uvx (zero install, recommended)
Requires uv. One command and you're done:
uvx hyperstore-mcpOption B — pipx
pipx install hyperstore-mcp
hyperstore-mcpOption C — Docker (for remote hosting)
docker run --rm -p 8080:8080 ghcr.io/deficlow/hyperstore-mcp
# Now MCP Streamable HTTP at http://localhost:8080/mcpOption D — Hosted endpoint (no install)
Use our managed Streamable HTTP server:
https://mcp.store.hypergpt.ai/mcpConnect from your LLM client
Claude Desktop
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 Code
claude mcp add hyperstore -- uvx hyperstore-mcpCursor
.cursor/mcp.json (project) or ~/.cursor/mcp.json (global):
{
"mcpServers": {
"hyperstore": {
"command": "uvx",
"args": ["hyperstore-mcp"]
}
}
}Windsurf
~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"hyperstore": {
"command": "uvx",
"args": ["hyperstore-mcp"]
}
}
}Cline (VS Code)
settings.json:
{
"cline.mcpServers": {
"hyperstore": {
"command": "uvx",
"args": ["hyperstore-mcp"]
}
}
}Zed
~/.config/zed/settings.json:
{
"context_servers": {
"hyperstore": {
"command": {
"path": "uvx",
"args": ["hyperstore-mcp"]
}
}
}
}Gemini CLI
~/.gemini/settings.json:
{
"mcpServers": {
"hyperstore": {
"command": "uvx",
"args": ["hyperstore-mcp"]
}
}
}ChatGPT (Pro / Team / Enterprise)
Settings → Connectors → Add custom connector:
Name: HyperStore
MCP Server URL:
https://mcp.store.hypergpt.ai/mcpAuthentication: None
OpenAI Responses API
from 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)Anthropic Messages API
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.
Self-hosting
For self-hosting, use the Docker image.
For direct invocation without Docker, the CLI accepts --transport http|sse
(see hyperstore-mcp --help).
Configuration
When self-hosting, these environment variables can be set
(see .env.example for the full list):
Variable | Default | Purpose |
|
| Bind host (http/sse transports) |
|
| Bind port (http/sse transports) |
|
| Logging level ( |
Development
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 testingInspect the running server with the official MCP Inspector:
npx @modelcontextprotocol/inspector uvx hyperstore-mcpHow it works
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/apiLicense
MIT © HyperGPT
Available Tools
8 toolsai_searchA
Natural-language semantic search powered by embeddings. Best for fuzzy intent ('a tool that helps me write Python tests', 'something like Midjourney but free'). Returns up to 12 apps ranked by semantic similarity.
Inputs: prompt (required).
browse_appsA
Browse apps A-Z by starting letter. Use letter='#' for apps starting with digits or symbols. Useful for alphabetical discovery rather than search.
Inputs: letter (required), pricing, cursor, limit.
category_appsA
Get apps within a specific category. Returns the category metadata plus a paginated list of apps in that category, sorted by popularity.
Inputs: slug (required), pricing, cursor, limit.
get_appA
Fetch the full detail page for a single AI app by slug: long description, features, screenshots, categories, pricing, rating, website URL, source attribution.
Inputs: slug (required).
get_homepageA
Fetch the HyperStore homepage payload: top categories with their featured apps, the trending apps strip, and totals. Good first call to give the user a broad overview.
Inputs: page.
list_appsA
Paginated apps listing with optional filters. Combine category, pricing, and a free-text query to drill down. Returns apps sorted by popularity. Use cursor (last app id from previous page) to paginate.
Inputs: category, pricing, query, cursor, +1 more.
list_categoriesA
List all HyperStore categories with app counts. Use this first when the user asks 'what kinds of AI tools are there?' or to discover available category slugs.
Inputs: no named parameters. Returns: result
search_appsA
Search HyperStore's AI apps directory by keyword. Returns a paginated list of matching apps with name, slug, short description, pricing, and rating. Use this when the user gives concrete keywords (e.g. 'image upscaler', 'code copilot').
Inputs: query (required), limit, cursor.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 8 tools
Each tool has a clearly distinct purpose: ai_search for semantic search, search_apps for keyword search, browse_apps for alphabetical browsing, category_apps for category browsing, get_app for details, get_homepage for overview, list_apps for filtered paginated listing, and list_categories for category listing. No overlap.
All tool names follow a consistent verb_noun pattern in snake_case: `ai_search`, `browse_apps`, `category_apps`, `get_app`, `get_homepage`, `list_apps`, `list_categories`, `search_apps`. No deviations.
8 tools is an appropriate number for an app directory MCP server. Each tool covers a specific access pattern (search, browse, detail, categories, homepage) without being overly many or too few.
The tool set provides comprehensive coverage for an AI apps directory: multiple search methods (semantic and keyword), multiple browse methods (alphabetical, category, paginated filters), detail retrieval, category listing, and homepage overview. No obvious gaps for a read-only directory.
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