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HyperStore MCP

HyperStore MCP

by deficlow

HyperStore MCP

Plug 6,500+ AI apps into any LLM via the Model Context Protocol.

PyPI Glama Smithery MCP Registry CI License: MIT

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

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 app

  • hyperstore://category/{slug} — top apps in a category

  • hyperstore://catalog — full category index

3 prompts:

  • find_tool_for_task — guided discovery for a task

  • compare_apps — side-by-side app comparison

  • discover_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-mcp

Option B — pipx

pipx install hyperstore-mcp
hyperstore-mcp

Option C — Docker (for remote hosting)

docker run --rm -p 8080:8080 ghcr.io/deficlow/hyperstore-mcp
# Now MCP Streamable HTTP at http://localhost:8080/mcp

Option D — Hosted endpoint (no install)

Use our managed Streamable HTTP server:

https://mcp.store.hypergpt.ai/mcp

Connect 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-mcp

Cursor

.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/mcp

  • Authentication: 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

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)


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 testing

Inspect the running server with the official MCP Inspector:

npx @modelcontextprotocol/inspector uvx hyperstore-mcp

How 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/api

License

MIT © HyperGPT

Available Tools

8 tools
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

    A4.2/5.0

    Scored across 8 tools

    Disambiguation5/5

    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.

    Naming Consistency5/5

    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.

    Tool Count5/5

    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.

    Completeness5/5

    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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