HMR Docs▌
by pyth-on-line
Access HMR Docs for Python: guides, examples, and source code on hot module replacement, hot reloading, and reactive pro
Provides access to HMR (Hot Module Replacement) documentation for reactive programming and hot reloading in Python through a streamable HTTP protocol endpoint, serving guides, examples, conceptual explanations, architecture overviews, and complete library source code.
github stars
★ —
best for
- / Python developers learning reactive programming
- / Engineers implementing hot module replacement
- / Developers studying HMR library internals
capabilities
- / Learn HMR library basics and concepts
- / View complete HMR core source code
- / Access HMR unit tests and code examples
- / Browse reactive programming guides
- / Study hot reloading architecture
what it does
Provides comprehensive documentation, source code, and examples for the HMR (Hot Module Replacement) library for reactive programming and hot reloading in Python.
about
HMR Docs is a community-built MCP server published by pyth-on-line that provides AI assistants with tools and capabilities via the Model Context Protocol. Access HMR Docs for Python: guides, examples, and source code on hot module replacement, hot reloading, and reactive pro This server exposes 3 tools that AI clients can invoke during conversations and coding sessions.
how to install
You can install HMR Docs 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
HMR Docs 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 HMR Docs MCP server?
- HMR Docs 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 HMR Docs?
- This profile displays 44 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.
Ratings
4.7★★★★★44 reviews- ★★★★★Maya Ghosh· Dec 24, 2024
HMR Docs reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Ren Farah· Dec 24, 2024
Strong directory entry: HMR Docs surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Dhruvi Jain· Dec 20, 2024
HMR Docs has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Omar Bansal· Dec 20, 2024
We wired HMR Docs into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Ren Perez· Nov 15, 2024
I recommend HMR Docs for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Oshnikdeep· Nov 11, 2024
We evaluated HMR Docs against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Diya Singh· Nov 11, 2024
HMR Docs is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Nia Haddad· Nov 11, 2024
HMR Docs is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Mei Dixit· Oct 6, 2024
According to our notes, HMR Docs benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Ganesh Mohane· Oct 2, 2024
We wired HMR Docs into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
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