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Historical Weather (past N days)

weather_historical
Read-onlyIdempotent

Last 1-365 days of daily observations with pre-computed aggregates (frost-day count, summer-day count, rainy days, heavy-rain days, extremes). Answers 'is this a normal year?' questions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNo
lonNo
unitsNoimperial
zip_codeNo
days_backNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare the tool read-only, idempotent, and non-destructive, so the bar is lower. The description adds useful behavioral context by mentioning daily observations and pre-computed aggregates such as frost-day count and extremes. However, it does not disclose return structure, handling of missing data, timezone behavior, or how lat/lon vs zip_code location resolution works, leaving some behavioral gaps.

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 focused sentences with no filler. The first sentence states range and content, and the second gives a clear use case. It is appropriately front-loaded and concise.

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

Completeness2/5

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

With five optional parameters, no output schema, and zero schema-coverage, the description must carry more weight. It conveys output type and use case but omits essential invocation details like how to specify location, what units mean for aggregates, and what an agent should expect in the response. This is not complete enough for correct first-time use.

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

Parameters2/5

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

Schema description coverage is 0%, placing the full burden on the description to explain five parameters, but it only implicitly references days_back via 'last 1-365 days'. It does not explain units, lat/lon, zip_code, or the relationship between location parameters, so an agent gets little help beyond the bare schema.

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

Purpose4/5

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

The description clearly names a specific resource ('daily observations' over the 'last 1-365 days') and distinguishes the tool from weather_current, weather_forecast, and weather_normals by presenting historical coverage and 'normal year' questions. It lacks an explicit contrast with weather_degree_days or other weather siblings, so it is clear but not fully differentiated.

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?

The description gives a concrete use case: answering 'is this a normal year?' questions, which signals when to use this tool instead of current or forecast weather tools. It does not provide explicit 'when not to use' or name alternatives, so it stops short of full routing guidance.

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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