City geocoding and timezone resolution for birth data, for AI agents, one API key.
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Average 4.4/5 across 3 of 3 tools scored.
The three tools serve distinct purposes (country list, city list, place search), but the name 'get_location_countries_iso2' misleadingly suggests it returns country details rather than cities, creating potential confusion with 'get_location_countries'.
All tools share the 'get_location_' prefix, but the suffixes are inconsistent: one describes an action ('search') while two describe resources ('countries', 'countries_iso2'), and the latter does not match its actual output (cities).
Three tools is well-scoped for a location/timezone resolution server, covering country discovery, city listing, and place search without redundancy.
The core workflow of place name to coordinates/timezone is covered (country → city → search), but there is no reverse geocoding from coordinates or a direct timezone lookup, leaving a minor gap.
get_location_countriesList all countries - ISO codes and city coverageAReturns every country with ISO 3166-1 alpha-2 and alpha-3 codes, plus the number of searchable cities per country. Use this endpoint to build country dropdown menus, regional filters, or to check city coverage before querying. Sorted alphabetically by country name. Covers Europe, Americas, Asia, Middle East, Africa, and Oceania.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum items to return per page. Range: 1-250, default 50. | |
| offset | No | Number of items to skip for pagination. Default 0. | |
| compact | No | Set true for the same data in a compact shape: arrays of same-shaped objects arrive columnar as {"__cols":[names],"__rows":[[values]]}. Lossless, typically 40 to 52 percent fewer tokens. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it is safe. The description adds meaningful behavioral context beyond this: results are sorted alphabetically, cover specific global regions, and include city counts. This adds value without contradicting annotations.
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?
Three sentences, no wasted words. The first sentence states the core output, the second gives concrete use cases, and the third adds sorting and regional scope. Information is front-loaded and structured.
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 list tool with no output schema and 3 optional pagination/format parameters, the description is complete enough: it covers purpose, use cases, sorting, and coverage. It does not explain pagination behavior, but the schema provides that, and the tool's simplicity does not demand more.
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?
Schema description coverage is 100%, so all three parameters (limit, offset, compact) are fully documented in the schema. The description does not add additional parameter semantics, and with full schema coverage the baseline of 3 is appropriate.
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 the tool returns all countries with ISO alpha-2/alpha-3 codes and city coverage, using a specific verb ('Returns') and resource ('every country'). It implicitly distinguishes from the sibling get_location_countries_iso2, which likely returns only ISO2 codes, and from get_location_search by focusing on list-level metadata.
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 description explicitly names use cases: building country dropdown menus, regional filters, and checking city coverage before querying. It does not explicitly mention when not to use it or name alternatives, but the use cases are clear and well-contextualized, making sibling differentiation implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_location_countries_iso2Get cities in a country - Geocoding directory sorted by populationAReturns all cities for a specific country, identified by ISO 3166-1 alpha-2 code (e.g. DE for Germany, FR for France, GB for United Kingdom, US for United States). Each city includes geographic coordinates, IANA timezone, and DST-aware UTC offset for direct use in astrology birth chart, horoscope, transit, and panchang calculations. Cities sorted by population with the largest metropolitan areas first.
| Name | Required | Description | Default |
|---|---|---|---|
| iso2 | Yes | ISO 3166-1 alpha-2 country code, case-insensitive. Common codes: DE (Germany), FR (France), GB (United Kingdom), US (United States), ES (Spain), IT (Italy), NL (Netherlands), IN (India), BR (Brazil), JP (Japan). | |
| limit | No | Maximum items to return per page. Range: 1-100, default 20. | |
| offset | No | Number of items to skip for pagination. Default 0. | |
| compact | No | Set true for the same data in a compact shape: arrays of same-shaped objects arrive columnar as {"__cols":[names],"__rows":[[values]]}. Lossless, typically 40 to 52 percent fewer tokens. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral context by specifying that each city includes geographic coordinates, IANA timezone, DST-aware UTC offset, and that cities are sorted by population descending. It does not mention pagination or rate limits, but these are not critical given the annotations.
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 three sentences, front-loaded with the core action, followed by data field details and ordering. Every sentence provides value—what it returns, what data is included, and how results are sorted—without any fluff.
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 read-only tool with 4 parameters and no output schema, the description covers purpose, data fields, ordering, and use case. Pagination is handled by schema parameters, so the description does not need to explain it. It could arguably mention that results are paginated, but the schema makes that inferable.
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?
Schema description coverage is 100%, and the schema already documents all parameters (iso2, limit, offset, compact) with examples and constraints. The description repeats the country code examples but adds no new parameter-level semantics beyond what the schema provides, so it stays at the baseline.
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?
Description uses specific verb 'Returns' with resource 'all cities for a specific country', clearly identifying the tool's action. It distinguishes from sibling tools by focusing on country-code-based lookup and population-sorted output, as seen in the title and description.
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 description clearly indicates this tool is for retrieving cities by ISO country code, with examples and use cases (astrology calculations). However, it does not explicitly contrast with sibling tools like get_location_search or state when NOT to use it, so exclusions are implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_location_searchSearch cities worldwide - Geocoding autocomplete with coordinates and timezoneATurn a place name into coordinates, an IANA timezone and a DST-aware UTC offset, across 235,000+ cities and towns in 240+ countries. Coverage reaches rural towns of a few hundred people and every administrative seat, so a birthplace outside a major metro resolves as reliably as a capital. Matching is case-insensitive, accent-insensitive and partial, so ber matches Berlin, Bern and Bergen, native scripts are transliterated, and historic names resolve to the current place, so bombay returns Mumbai and peking returns Beijing. Results are ordered by match quality first and population second, so an exactly named small town is never buried under a larger city that merely shares its opening letters. Built for birth chart location pickers, horoscope apps, event scheduling, and any feature that needs place-to-coordinates resolution.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Place to search for, written the way a person would. Accepts a bare city (berlin), a city plus country (berlin germany), a comma-qualified place (richfield, utah), a fully qualified place (richfield, utah, united states), or a historic name (bombay, peking, constantinople). Commas are optional, and a qualifier the dataset spells differently, such as USA for United States, still resolves. Matched against city name, alternate names, state or province, and country. Add the state or country whenever the name is common, since that is what separates the six Springfields, and Richfield, Utah from Richfield, Minnesota. | |
| limit | No | Maximum items to return per page. Range: 1-50, default 10. | |
| offset | No | Number of items to skip for pagination. Default 0. | |
| compact | No | Set true for the same data in a compact shape: arrays of same-shaped objects arrive columnar as {"__cols":[names],"__rows":[[values]]}. Lossless, typically 40 to 52 percent fewer tokens. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds substantial behavioral context: matching is case-insensitive, accent-insensitive, and partial; native scripts are transliterated; historic names resolve; results are ordered by match quality then population. This goes well beyond the structured annotations, providing a rich picture of expected behavior.
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 length-dense but every sentence serves a purpose: it front-loads the core action, then adds coverage details, matching rules, ordering logic, and target use cases. There is no filler or redundancy, making it appropriately sized for a tool with this complexity.
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?
Given the tool's search complexity and the absence of an output schema, the description covers the necessary ground: it states the output type (coordinates, timezone, offset), explains coverage breadth, matching behavior, and result ordering, and provides real-world examples. It does not need to explain return structure since that is not in scope; the description is complete for selecting and invoking the tool correctly.
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?
Schema description coverage is 100%, so the baseline is 3. The description adds significant value beyond the schema, especially for 'q' with detailed guidance on quoting, comma usage, geographic qualifiers, and examples like 'bombay returns Mumbai'. It also explains the interplay between the 'q' parameter and result disambiguation, which the schema alone doesn't convey.
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 opens with a specific verb+resource: 'Turn a place name into coordinates, an IANA timezone and a DST-aware UTC offset' and amplifies scope with '235,000+ cities and towns in 240+ countries.' This clearly distinguishes it from the sibling country-list tools, making the purpose unambiguous.
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 description states its intended use cases ('Built for birth chart location pickers, horoscope apps, event scheduling, and any feature that needs place-to-coordinates resolution') and implies when to use it. However, it does not explicitly mention when not to use it or name alternatives, though the sibling tools are about country lists, so the context is reasonably clear.
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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