AQUAVIEW▌
by aquaview
AQUAVIEW — Search and access global oceanographic and environmental datasets quickly. Discover maps, measurements, and d
Search and access global oceanographic and environmental datasets
github stars
★ —
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Marine researchers studying ocean conditions
- / Environmental scientists analyzing climate data
- / Developers building oceanographic applications
capabilities
- / Search oceanographic datasets by location and parameters
- / Get detailed metadata for marine datasets
- / Fetch file listings from ocean monitoring stations
- / Access atmospheric and marine environmental data
what it does
Provides access to global oceanographic and environmental datasets from NOAA sources like GCOOS, WOD, and IOOS. Search and retrieve marine data for research or analysis.
about
AQUAVIEW is an official MCP server published by aquaview that provides AI assistants with tools and capabilities via the Model Context Protocol. AQUAVIEW — Search and access global oceanographic and environmental datasets quickly. Discover maps, measurements, and d This server exposes 3 tools that AI clients can invoke during conversations and coding sessions.
how to install
You can install AQUAVIEW 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
AQUAVIEW 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 AQUAVIEW MCP server?
- AQUAVIEW 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 AQUAVIEW?
- This profile displays 53 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.
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★53 reviews- ★★★★★Shikha Mishra· Dec 28, 2024
We wired AQUAVIEW into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Min Sethi· Dec 28, 2024
According to our notes, AQUAVIEW benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Neel Mensah· Dec 28, 2024
We wired AQUAVIEW into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Ira Lopez· Dec 20, 2024
Useful MCP listing: AQUAVIEW is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Ganesh Mohane· Dec 4, 2024
Useful MCP listing: AQUAVIEW is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Nikhil Bansal· Dec 4, 2024
Strong directory entry: AQUAVIEW surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Daniel Diallo· Nov 23, 2024
I recommend AQUAVIEW for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Yash Thakker· Nov 19, 2024
AQUAVIEW is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Nikhil Bhatia· Nov 19, 2024
We evaluated AQUAVIEW against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Li Flores· Nov 19, 2024
AQUAVIEW is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
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