// may the 4th be with you⚔️

HeyOnCall

by heyoncall

HeyOnCall sends automated phone notifications via a hosted paging service to alert on-call teams when long-running tasks

Sends phone notifications through HeyOnCall's hosted paging service to alert users when long-running tasks are completed or assistance is needed.

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Phone-based notificationsHosted paging service

best for

  • / Developers running long builds or deployments
  • / System administrators monitoring processes
  • / Anyone needing phone alerts for automated tasks

capabilities

  • / Send phone notifications to alert users
  • / Page users when long-running tasks complete
  • / Trigger alerts when assistance is needed
  • / Deliver notifications through hosted paging service

what it does

Sends phone notifications through HeyOnCall's paging service to alert you when tasks complete or when you need to be notified.

about

HeyOnCall is an official MCP server published by heyoncall that provides AI assistants with tools and capabilities via the Model Context Protocol. HeyOnCall sends automated phone notifications via a hosted paging service to alert on-call teams when long-running tasks

how to install

You can install HeyOnCall in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server supports remote connections over HTTP, so no local installation is required.

license

MIT

HeyOnCall is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

FAQ

What is the HeyOnCall MCP server?
HeyOnCall is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for HeyOnCall?
This profile displays 39 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.8 out of 5—verify behavior in your own environment before production use.

Discussion

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MCP server reviews

Ratings

4.839 reviews
  • Pratham Ware· Dec 28, 2024

    HeyOnCall is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Carlos Rao· Dec 24, 2024

    Useful MCP listing: HeyOnCall is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Ren Flores· Dec 16, 2024

    HeyOnCall is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Advait Johnson· Dec 8, 2024

    Strong directory entry: HeyOnCall surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Maya Srinivasan· Nov 27, 2024

    HeyOnCall has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Yash Thakker· Nov 19, 2024

    We evaluated HeyOnCall against two servers with overlapping tools; this profile had the clearer scope statement.

  • Advait Haddad· Nov 15, 2024

    According to our notes, HeyOnCall benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Carlos Thomas· Nov 11, 2024

    HeyOnCall reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Meera Agarwal· Nov 7, 2024

    We wired HeyOnCall into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Meera Desai· Oct 26, 2024

    According to our notes, HeyOnCall benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

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