US Drought Monitor

get_drought_history

Get historical drought conditions over time from the US Drought Monitor.

Returns weekly drought severity percentages for the specified area and date range.
The Drought Monitor is updated every Tuesday, so data points are weekly.

Args:
    state: Two-letter US state abbreviation (e.g. 'CA', 'TX').
    county_fips: Five-digit county FIPS code (e.g. '06037' for Los Angeles County).
    start_date: Start date in YYYY-MM-DD format. Defaults to one year ago.
    end_date: End date in YYYY-MM-DD format. Defaults to today.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`state`	No
`end_date`	No
`start_date`	No
`county_fips`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.3/5.0

Behavior4/5

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

Without annotations, the description carries the full burden. It transparently discloses the data source (US Drought Monitor), the update schedule (every Tuesday), and the return type (weekly severity percentages). It clearly implies a read-only operation. It does not mention limitations like data start date or pagination, but the presence of an output schema reduces the need to explain return structure.

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?

The description is concise and front-loaded. The first sentence states the core purpose, followed by what is returned and update cadence, then a structured Args list. Every sentence contributes useful information with no fluff.

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

Completeness4/5

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

For a data retrieval tool with an output schema, the description is largely complete: it covers the data source, update frequency, and parameter formats. However, it does not explicitly note that parameters are optional or whether state/county_fips are mutually exclusive, which would enhance completeness. The output schema handles return values, so no gap there.

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?

The input schema has zero descriptions, so the description must compensate. It does so thoroughly with an Args section explaining each parameter (state, county_fips, start_date, end_date) including format and examples (e.g., 'CA', '06037', 'YYYY-MM-DD'). This fully adds meaning beyond the schema.

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?

The description clearly states the tool's purpose with a specific verb and resource: 'Get historical drought conditions over time from the US Drought Monitor.' It further specifies that it returns weekly drought severity percentages, which distinguishes it from sibling tools like get_current_drought (current conditions) and get_drought_summary (summary data).

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

Usage Guidelines3/5

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

The description implies usage for historical time-series data by emphasizing 'over time' and 'weekly' data points, but it does not explicitly mention when to use this tool versus the siblings or provide exclusion criteria. No alternatives are named, so guidance is implied rather than explicit.

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

All US Drought Monitor tools Inspect

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TDQS

A4.2/5.0

Disambiguation3/5

get_current_drought and get_drought_summary both return national-level current drought percentages when no location is specified, creating potential overlap. However, their intended use cases (location-specific data vs national summary) are mostly clear, so the confusion is limited.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with the get_ prefix, making the API predictable and easy to navigate.

Tool Count5/5

Three tools cover current conditions, historical trends, and national summary—each earns its place for a focused drought data server. This is within the typical 3-15 tool range.

Completeness4/5

The server provides current, historical, and summary data, covering the main drought monitoring needs. A minor gap is that get_drought_history explicitly mentions state and county FIPS but does not state whether national-level historical data is available, which could be an oversight.

Resources

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