v1_timezoneTimezone by coordinate: Coordinates → IANA timezone, UTC offset, local time. Source: Open-Meteo. $0.005 per call · GET /v1/timezone
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | Latitude. Example: '48.85'. | |
| lon | Yes | Longitude. Example: '2.35'. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / lat / examplesAdded value: +[
+ 48.85
+]Input schema / properties / lon / examplesAdded value: +[
+ 2.35
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds useful operational traits such as the data source (Open-Meteo), per-call cost, and HTTP method, but does not go into deeper behaviors like DST handling or response format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely compact and front-loaded. The core mapping is stated in one sentence, followed by source, cost, and endpoint details. Every element earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter read-only lookup with full schema coverage, the description is complete. It lists the expected outputs, compensating for the missing output schema, and adds source, pricing, and endpoint context. No critical operational detail appears to be missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides 100% coverage for both lat and lon parameters, each with descriptions and examples. The description only refers to 'Coordinates' generically and does not add any parameter-level meaning beyond what the schema already documents.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a coordinate-to-timezone transformation and enumerates the specific outputs (IANA timezone, UTC offset, local time). The phrase 'by coordinate' distinguishes it from sibling location-related tools like v1_geocode or v1_reverse_geocode.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended usage is clear: use this tool when you have latitude/longitude coordinates and need timezone information. It does not explicitly name alternatives or exclusion conditions, but the context is strong enough to guide an agent without confusion.
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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Each tool maps to a distinct public data source and lookup type, from weather and geocoding to legal codes and vehicle VINs. Even adjacent tools like weather vs. weather_alerts or geocode vs. reverse_geocode are clearly separated by resource and direction.
All tools share a consistent v1_ prefix and snake_case resource naming, making the pattern predictable. Minor inconsistencies exist: most names are noun phrases (v1_country, v1_stock_quote) while a few are verb-led (v1_geocode, v1_validate_email, v1_unit_convert), and some abbreviations like v1_cfr and v1_lei are less descriptive.
At 31 tools, the surface is large and will strain agent tool-selection, even though each tool is individually useful. The broad 'public data utility' scope explains the count, but the calibration threshold of 25+ tools indicates an oversized set for practical use.
For a lookup-oriented public data utility, the set covers a wide range of common needs—weather, finance, location, legal/medical codes, domain/network, and conversions—without dead ends. It lacks some obvious public data categories (e.g., web search, population/census, news) and enumeration endpoints, but agents can work around these gaps.