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clm_get_weather

Read-onlyIdempotent

Get daily weather records for a site over a date range, including temperature, wind speed, and weather symbol. Use optional fields or site ID to tailor results to your needs.

Instructions

Daily weather records (temperature, wind speed, symbol) stored for a site within a date range.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsNoOnly include these top-level fields in each returned row.
site_idNoSite to query. Defaults to the authenticated caller's own site.
end_dateYesEnd of the date range.
start_dateYesStart of the date range.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
noteNo
returnedYes
truncatedYes
total_countNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds useful scoping context (daily records, site, date range, data fields), but it does not disclose details like default site behavior, timezone handling, or empty-result behavior. No contradiction with 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?

The description is a single sentence with no filler, front-loading the key resource and constraints. Every part contributes to understanding what the tool returns and for whom.

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 read-only query tool with an output schema and 100% parameter schema coverage, the description is largely complete: it names the resource, scope, and data fields. Minor details like units, timezone, or date inclusiveness are not mentioned, but the structured schema and annotations cover most operational context.

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%, so the schema already documents all four parameters clearly. The description adds the field names 'temperature, wind speed, symbol', which gives some hints about the fields parameter, but it does not meaningfully extend the schema's documentation of start_date, end_date, or site_id. Baseline 3 is appropriate.

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 identifies the unique resource (daily weather records) and the scope (site + date range), plus the contained data fields (temperature, wind speed, symbol). It is phrased as a noun fragment rather than an explicit action verb like 'retrieves', but the tool name and 'stored for a site within a date range' make the retrieval intent clear. There is no sibling weather tool, so sibling differentiation is not a concern.

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 when to use the tool: when weather records for a site and date range are needed. However, it provides no explicit guidance about alternatives, exclusions, or when not to use it. Since no other weather-focused sibling exists, the usage context is inferable but not stated.

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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