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usgs-water-mcp-server

by io.github.cyanheads

Water Get Series

water_get_series
Read-onlyIdempotent

Get a daily or instantaneous time series for one USGS site and parameter over a date range, as time-ordered value records. Large sets (>500 records) return the most recent 500 with truncated=true; with DataCanvas enabled they instead spill to a canvas (canvas_id/table_name) for SQL via water_dataframe_query. Use water_find_sites and water_list_parameters to resolve inputs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
siteYesUSGS site number (8–15 digits, e.g. "01646500" for Potomac River at Little Falls). Use water_find_sites to discover valid site numbers.
endDateYesEnd date in YYYY-MM-DD format (e.g. "2024-12-31").
canvas_idNoCanvas ID from a prior water_get_series call to append data to an existing canvas rather than creating a new one. Omit to start a fresh canvas.
startDateYesStart date in YYYY-MM-DD format (e.g. "2024-01-01").
seriesTypeNo"daily" returns one value per day (DV service, typically mean/max/min). "instantaneous" returns ~15-minute readings (IV service). Default: "daily". Use "instantaneous" for high-resolution analysis.daily
parameterCdYesA single 5-digit USGS parameter code (e.g. "00060" for discharge, "00065" for gage height). One code per call — this tool returns one series. Use water_list_parameters to discover available codes.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
queryNoQuery parameters used for this request.
noticeNoAdvisory when the result was truncated — narrow the date range or enable DataCanvas for full access.
valuesNoTime-ordered value records. Contains all records when not truncated, or the most recent 500 when truncated (no canvas) or a preview slice (with canvas).
siteNameNoHuman-readable USGS site name.
unitCodeNoUnit of measure for all values in this series (e.g. "ft3/s", "ft").
canvas_idNoCanvas ID for the DataCanvas holding the full time series. Present only when truncated=true and DataCanvas is enabled. Pass to water_dataframe_describe then water_dataframe_query.
truncatedNoTrue when the result exceeds 500 records and was trimmed. Query the full series via water_dataframe_query when canvas_id is present, or narrow the date range.
seriesTypeNo"daily" = one value per day (DV service); "instantaneous" = ~15-minute readings (IV service).
siteNumberNoUSGS site number (8–15 digits, e.g. "01646500").
table_nameNoDuckDB table name in the canvas holding all records. Present when canvas_id is present. Use as the FROM target in water_dataframe_query SQL.
parameterCdNo5-digit USGS parameter code (e.g. "00060" for discharge).
totalRecordsNoTotal number of records in the upstream result set (before any truncation).
parameterNameNoHuman-readable parameter name with units (e.g. "Streamflow, ft³/s").

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations provide readOnlyHint, openWorldHint, idempotentHint. Description adds valuable behavioral context: truncation behavior for large sets, canvas spillover with DataCanvas enabled, and reference to SQL query via water_dataframe_query. This goes beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, dense and front-loaded. Each sentence contributes: what, behavior on large sets, and how to resolve inputs. No fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given output schema exists, description doesn't need return details. Covers truncation, canvas spillover, and input resolution. Sufficient for an agent to call correctly without knowing specific response structure.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds meaning: clarifies truncation as a consequence of large sets and canvas spillover. It explains that canvas_id is for appending to existing canvas. The description complements schema details with usage semantics, especially around truncation and canvas behavior.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (get), resource (time series for one USGS site and parameter), and scope (daily/instantaneous over a date range). It distinguishes from siblings by mentioning water_find_sites and water_list_parameters as resolving inputs, and by referencing water_dataframe_query for canvas spillover.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Clear context for when to use: it returns daily or instantaneous series for one site/parameter. It doesn't explicitly say when not to use it vs. water_get_conditions or water_get_readings, but it does reference sibling tools for resolving inputs. Slight gap in exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.5/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: site discovery, parameter lookup, instantaneous readings, time series, conditions, and dataframe analysis. No overlapping functionality.

Naming Consistency4/5

All tools start with 'water_' and mostly follow a verb_noun pattern (e.g., water_find_sites, water_get_readings). The dataframe tools (water_dataframe_describe, water_dataframe_query) use a noun_verb structure, which is a minor deviation.

Tool Count5/5

7 tools is well-scoped for the domain, covering essential operations without excess or deficiency.

Completeness4/5

The set covers key workflows: site discovery, parameter lookup, data retrieval (instantaneous, series, conditions), and analysis. Missing a dedicated tool for detailed site metadata, but find_sites provides reasonable coverage.

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