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HyperStore MCP Server

Developer ToolsModerate5.2MCP RegistryLocalRemote
Free

Server data from the Official MCP Registry

Search and inspect 6,500+ curated AI apps from the HyperStore directory.

About

Search and inspect 6,500+ curated AI apps from the HyperStore directory.

Remote endpoints: streamable-http: https://mcp.store.hypergpt.ai/mcp sse: https://mcp.store.hypergpt.ai/sse

Security Report

5.2
Moderate5.2Moderate Risk

HyperStore MCP is a well-structured, read-only wrapper around a public REST API with no authentication requirements or sensitive operations. The codebase demonstrates good security practices: proper error handling, input validation via Pydantic, context managers for resource cleanup, and no credential storage. Permissions align well with the server's purpose (read-only API calls to HyperStore). Minor code quality observations exist but do not present meaningful security risks. Supply chain analysis found 3 known vulnerabilities in dependencies (0 critical, 3 high severity). Package verification found 1 issue.

7 files analyzed · 7 issues found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

HTTP Network Access

Connects to external APIs or services over the internet.

env_vars

Check that this permission is expected for this type of plugin.

How to Install & Connect

Available as Local & Remote

This plugin can run on your machine or connect to a hosted endpoint. during install.

Documentation

View on GitHub

From the project's GitHub README.

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:

ToolPurpose
search_appsFull-text keyword search
ai_searchEmbedding-based semantic search
get_appFull app detail (features, screenshots, pricing)
list_appsPaginated apps with filters (category, pricing)
list_categoriesBrowse all 30+ categories
category_appsApps within a category
browse_appsA-Z directory listing
get_homepageTrending + top categories overview
get_alternativesCurated alternatives to an app
list_audiencesAudience segments (developers, lawyers, …)
apps_for_audienceBest AI tools for an audience
list_use_casesUse-case taxonomies (legal-contracts, …)
apps_for_use_caseAI 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

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):

VariableDefaultPurpose
MCP_HOST0.0.0.0Bind host (http/sse transports)
MCP_PORT8080Bind port (http/sse transports)
LOG_LEVELINFOLogging 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

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HyperStore MCP Server - Search and inspect 6,500+ curated AI apps from the | MCP Marketplace