river-level-mcp
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Alternatives to river-level-mcp
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- AlicenseNot gradedqualityBmaintenanceEnables AI agents to query live and historical USGS water data — real-time streamflow, gage height, temperature, precipitation, turbidity and other parameters — for individual gauge sites, and to discover which gauges are actively reporting a given parameter across a US state. Runs either as a keyless local stdio server or through a hosted gateway endpoint, with daily-value time series for trends and hydrographs.211 npmMIT
- AlicenseAqualityAmaintenanceProvides hydrology data (river levels, streamflow, flood forecasts, water quality) from USGS, NOAA, and SWOT, preserving units, datums, timezones, and data quality for AI agents.1365 PyPIMIT
- AlicenseAqualityCmaintenanceProvides AI agents live access to USGS stream flow, gage height, and water temperature data, along with EPA water quality results ranked against compliance thresholds, and composite basin health scores.3MIT
- AlicenseAqualityBmaintenanceEnables AI assistants to query live US lake and reservoir water levels from public agency gauges, including current elevation, historical trends, and side-by-side comparisons without an API key.4310 npmMIT
- AlicenseAqualityCmaintenanceEnables querying USGS water data including real-time and historical streamflow, gage height, and water temperature from USGS gauges across the United States.3MIT
- AlicenseNot gradedqualityBmaintenanceEnables querying live USGS stream gauge data by river name or location, retrieving current water-quality readings (temperature, pH, dissolved oxygen, turbidity), and ranking locations by EPA Clean Water Act compliance scores and grades. Supports comparing multiple watersheds best-to-worst, with no API keys required.MIT
TDQS
Scored across 4 tools
Each tool maps to a distinct temporal/functional role: find_river_gauges for discovery, get_river_level for current conditions, get_river_history for past trends, and get_flood_forecast for predictions. The only minor overlap is that get_river_level and get_flood_forecast both surface flood stages, but the primary purpose of each is clear and an agent can select confidently.
All tools follow a clean verb_noun pattern (find_river_gauges, get_river_level, get_river_history, get_flood_forecast) with the river/gauge domain object consistent across names. The find-vs-get distinction is semantically meaningful (search vs. retrieve) rather than arbitrary.
Four tools is well-scoped for a focused USGS gauge data server: discover, current reading, history, and forecast. Each earns its place with no redundant or filler tools.
The surface covers the full read lifecycle from gauge discovery through current readings, historical trends, and NWS forecasts, which addresses the common question types. Minor gaps remain, such as comparing multiple gauges in one call or basin-wide summaries, but agents can work around these via repeated queries.