lake-level-mcp
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Alternatives to lake-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.23 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.1363 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
- AlicenseNot gradedqualityBmaintenanceEnables querying current and historical relative water levels for 518 lakes, seas, and reservoirs worldwide from satellite radar altimetry, with search by name, country, or continent.5 npmMIT
- AlicenseNot gradedqualityFmaintenanceProvides NOAA tide predictions and observed water levels, allowing AI agents to query tide stations and current water level data.0MIT
- AlicenseNot gradedqualityCmaintenanceProvides lake conditions, fish-stocking records, fishing-favorability scoring, and live weather for outdoor recreation AI agents, powering trip-planning and fishing apps with 72,000+ US lakes and 293,000+ stocking events.MIT
TDQS
Scored across 4 tools
Each tool has a clearly distinct role: search_lakes discovers lakes, get_lake_level returns current conditions, get_lake_history returns time series, and compare_lakes returns multi-lake rankings. The only overlap is that compare_lakes includes current data, but its multi-lake comparative purpose is clearly distinct from the single-lake get_lake_level.
All four tools use consistent snake_case verb_noun naming: search_lakes, get_lake_level, get_lake_history, compare_lakes. The pattern is predictable and readable throughout.
Four tools are well-scoped for a focused lake-level domain. Each tool serves a distinct workflow—discovery, current reading, history, and comparison—with no obvious redundancy.
The surface covers the full read-only lifecycle: finding lakes, reading current levels, retrieving historical trends, and comparing multiple lakes. Search returns metadata such as slug, full-pool elevation, gauge agency, and page, so there are no obvious dead ends.