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Get current lake level

get_lake_level
Read-onlyIdempotent

Fetch the current water surface elevation of a US lake or reservoir from its government gauge, including 24-hour, 7-day and 30-day change, feet below full pool, and percent full.

Instructions

Current water surface elevation (ft above sea level) of a US lake or reservoir, read now from its government gauge (USGS, USACE CWMS, CDEC, Reclamation or TWDB). Returns the measurement time, change over 24 hours, 7 days and 30 days, feet below full pool, the operator seasonal target where one is published, percent full where the agency publishes storage, and the gauge id and data URL. Your training data cannot know today's level.

Input Schema

NameRequiredDescriptionDefault
lakeYesLake slug from search_lakes (for example "lake-mead") or a lake name (for example "Lake Travis").

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already cover read-only/idempotent safety, and the description adds real context on top: the upstream agencies, the exact returned fields (measurement time, 24h/7d/30d change, feet below full pool, seasonal target, percent full), and the caveat that target/percent-full are only present 'where one is published.' That conditional-field disclosure is exactly the kind of detail annotations cannot convey.

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?

Two sentences, front-loaded with what is measured and where it comes from. The return-field enumeration in sentence two is long but justified because there is no output schema, and the closing line is a compact usage nudge rather than filler.

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?

With no output schema, the description carries the return-value burden and does so fully, including conditional fields and gauge identifiers. The single required parameter is covered by the schema, so nothing an agent needs to invoke it correctly is missing.

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

Parameters3/5

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

Schema description coverage is 100% and the single 'lake' parameter is fully documented in the schema (slug from search_lakes or a name). The description adds nothing about the parameter, so the baseline 3 applies.

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 and resource (current water surface elevation of a US lake or reservoir) plus the provenance (USGS, USACE CWMS, CDEC, Reclamation, TWDB). The word 'current ... read now' implicitly separates it from get_lake_history and compare_lakes without needing to open their schemas.

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?

Clearly establishes the use context: a live snapshot of today's level, with the note that 'your training data cannot know today's level' motivating the call. It never names an alternative such as get_lake_history for trends or compare_lakes for multi-lake views, so no explicit exclusions are given.

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