Castles, fortresses, palaces and ruins worldwide: search, nearby, fame ranking, statistics. CC0.
Scored across 7 tools
Most tools occupy clearly separate niches: radius search, slug lookup, name search, ranked fame lists, random pick, country totals, and aggregate statistics. The one real overlap is that both list_countries and get_statistics explicitly claim to answer "which country has the most castles," which could cause misselection for that specific query. search_castles vs top_castles is also mildly close, but the name-substring requirement vs ranked-listing distinction is stated clearly.
Four tools follow a clean verb_noun pattern (get_castle, get_statistics, list_countries, search_castles), but three break it with noun-first or adjective-last forms (castles_near, random_castle, top_castles). 'castles_near' in particular inverts the natural order used everywhere else. All readable and snake_case throughout, but the pattern is mixed.
Seven tools is well-scoped for a read-only castle atlas: one lookup, one search, one geospatial query, one ranking, one random, one country list, one statistics endpoint. Each covers a distinct access pattern and none feels redundant padding.
The surface covers the main read paths for a data-browsing atlas: discovery (search, near, random, top), detail (get_castle), and aggregates (list_countries, get_statistics). Minor gaps: category is a filter on search_castles and top_castles, but there is no tool to enumerate valid categories/types, so an agent must guess values. No batch-fetch either, but that is a small limitation.
List landmarks within a radius of a WGS84 coordinate, nearest first, each with distance_km. Radius defaults to 100 km (max 2,000). For "castles near ", geocode the place yourself, then call this with its latitude/longitude.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 10) | |
| category | No | ||
| latitude | Yes | ||
| longitude | Yes | ||
| radius_km | No | Search radius in km (default 100) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description discloses key behaviors: results are sorted by distance, include 'distance_km', and the radius defaults/max are specified. It doesn't mention idempotency or side effects, but the listing nature implies read-only operation.
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?
Two concise sentences with essential information front-loaded. No filler or 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?
Given no output schema, the description adequately describes the output format (distance_km, sorted). It mentions the default/max radius and provides usage guidance. Minor omission: no mention of the 'limit' parameter's default behavior.
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 coverage is 40% (limit and radius_km have descriptions; latitude, longitude, category do not). The description adds radius defaults but does not clarify the missing parameters. It provides marginal value beyond the schema.
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 specifies the verb 'List' and the resource 'landmarks within a radius of a WGS84 coordinate', and clarifies the ordering ('nearest first'). It provides a concrete usage example ('For "castles near <place>"...'), which distinguishes the tool from siblings like 'search_castles' that likely use text-based queries.
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 gives explicit instructions for the common use case ('geocode the place yourself, then call this'), which aids selection. While it doesn't explicitly state when not to use this tool, the guidance is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch one landmark’s full record by slug (preferred, e.g. "palace-of-versailles") or exact name: coordinates, founding year and century, worldwide fame rank, photo URL with photographer credit and licence, Wikipedia link and its readership signals (Wikidata sitelinks, Wikipedia pageviews over the last 60 days summed across its largest language editions). Unsure of the slug? Call search_castles first.
| Name | Required | Description | Default |
|---|---|---|---|
| castle | Yes | Slug (preferred, e.g. "himeji-castle") or exact name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It clearly implies a read-only 'Fetch' operation and discloses the full set of return data, including licensing and readership signals. However, it does not mention error handling, not-found behavior, or rate limits, which prevents a perfect score.
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 a single dense paragraph with every clause adding value: it specifies the resource, input format, return fields, and usage guidance. No filler or redundant text is present.
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 getter with one parameter and no output schema, the description covers the input requirements, return value contents, and fallback to search_castles. It omits error cases or what happens if no match is found, but these are minor gaps for a fetch-by-identifier tool.
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 only says 'Slug (preferred, e.g. "himeji-castle") or exact name', but the description enriches this by showing the exact example 'palace-of-versailles', emphasizing the preferred format, and adding the fallback advice. This goes well beyond the schema's generic parameter description.
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 uses a specific verb ('Fetch') and resource ('one landmark's full record'), and lists the specific fields returned (coordinates, founding year, fame rank, photo, Wikipedia link, readership signals). It also distinguishes itself from search_castles by specifying that it takes a slug or exact name, making the tool's scope clear.
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 says 'Unsure of the slug? Call search_castles first', providing a clear alternative path. It also states the preferred input format (slug) vs exact name, giving direct guidance on when to use this tool versus searching.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Headline statistics computed live from the atlas: totals by type, top countries, busiest founding century, oldest landmarks, geographic extremes and the most famous entry. The source for claims like "which country has the most castles" or "when were castles built"; the full write-up lives at thecastlemap.com/statistics/.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It says 'computed live from the atlas' indicating real-time computation. It lists what statistics are included but doesn't specify performance or whether it's read-only. Still, it gives adequate behavioral context for a stateless read operation.
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?
Two sentences that are front-loaded with the main purpose and include specific examples. No wasted words.
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 no output schema, the description lists the categories of statistics (totals, top countries, etc.) and references a full write-up URL. It could detail the response format more, but for a simple stats tool it's fairly complete.
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?
There are no parameters, so the description provides all necessary semantic meaning by detailing what statistics the tool returns.
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 provides headline statistics computed live from the atlas, listing specific categories like totals by type, top countries, etc. It distinguishes itself from sibling tools by focusing on aggregate data rather than individual castles or searches.
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 says it's 'the source for claims like which country has the most castles', giving clear usage context. It does not explicitly state when not to use, but the alternative tools (get_castle, top_castles) are implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Every country in the atlas with its landmark count, most first — answers "which country has the most castles" (France leads). Returns country name and ISO code per row; country pages are browsable from the countries_index URL.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite lacking annotations, the description discloses key behavioral traits: it returns all countries sorted by count descending, includes country name and ISO code, and mentions a URL for browsing. This is sufficient for a simple read-only tool, though it lacks details on data freshness or limits.
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 concise at two sentences, front-loaded with the primary purpose and a practical use case. Every word adds value, 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?
Given the tool's simplicity (no parameters, no output schema, no annotations), the description is fully complete. It explains the output format, sorting, and provides an example answer, plus a link for further detail.
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?
There are no parameters (0 params), so the baseline score of 4 applies. The description adds no further parameter information, which is appropriate given the absence of parameters.
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 lists every country with its landmark count, sorted descending, and answers the specific question 'which country has the most castles'. The verb 'list' in the name and the explicit purpose differentiate it from siblings like search_castles or get_castle.
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 implies usage for obtaining a broad overview of countries with castle counts, but does not explicitly state when to use this tool versus alternatives like castles_near or top_castles. No when-not or exclusion criteria are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
One random landmark with its full record, optionally limited to a country — for discovery, quizzes and "castle of the day" features.
| Name | Required | Description | Default |
|---|---|---|---|
| country | No | Country name or ISO code (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description discloses key behaviors: returns one random record, optional country filter. Adequate for a simple read tool.
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?
Single sentence front-loads purpose and is perfectly concise with no filler.
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 simplicity (one optional param, no output schema), the description covers return type, optionality, and intended use completely.
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 coverage is 100% and parameter description already explains 'country' as optional. Tool description adds context ('optionally limited to a country') but minimal extra value.
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 it returns one random landmark with full record, optionally filtered by country. It distinguishes from siblings like get_castle (specific) and castles_near (proximity).
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?
Explicitly mentions use cases like discovery, quizzes, and 'castle of the day'. Implies when not to use via sibling tool names but lacks explicit exclusions or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the atlas’s 9,040 castles, fortresses, palaces and ruins by name (accent- and case-insensitive substring match). Optionally filter by country (name or ISO code) and category. Results come best-match-first, then by fame; each has coordinates, founding century, fame rank and links to its atlas page and Wikipedia. If nothing matches, retry with a shorter fragment of the name.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 10) | |
| query | Yes | Name or part of a name, e.g. "neuschwanstein" or "himeji" | |
| country | No | Country name or 2-letter ISO code (optional) | |
| category | No | Landmark type (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden, and it delivers: match semantics, result ordering (best-match first, then fame), the fields returned per hit, and the no-result fallback. It omits any note on pagination beyond limit or rate/cost behavior, which keeps it short of a 5.
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?
Four sentences, front-loaded with the scope and matching rule, then filters, then result shape, then failure handling. Every sentence contributes something distinct; nothing is filler.
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?
With no output schema and no annotations, the description still covers what an agent needs: what is searched, how matching works, how results are ordered, what each result contains, and what to do when empty. Nothing essential for correct invocation is 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?
Schema description coverage is 100%, so the schema already documents query, country, category and limit. The description mostly restates those (country name or ISO, category filter) and adds little parameter-level detail beyond the substring-matching note. Baseline 3 applies.
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?
States a specific verb (search) and resource (the atlas's 9,040 castles/fortresses/palaces/ruins) plus the matching semantics (accent- and case-insensitive substring). This clearly separates it from geo-based castles_near and id-based get_castle without opening a schema.
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?
Explains optional narrowing by country and category and gives a concrete recovery path when nothing matches ('retry with a shorter fragment'). It does not explicitly name when to prefer castles_near or top_castles instead, so the routing vs siblings is left partly implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The most famous landmarks worldwide or in one country, ordered by Castlemap fame rank — a blend of Wikipedia coverage and readership; rank 1 is the most famous (Great Wall of China). The direct answer to "most famous castles in "; filter by country and/or category.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many (default 10) | |
| country | No | Country name or ISO code (optional — omit for worldwide) | |
| category | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations to lean on, the description carries the transparency burden and does well by explaining the fame-rank ordering, the blending of Wikipedia coverage and readership, the anchor at rank 1, and the country/category scoping. It does not describe output fields or edge cases, but the essential behavior is transparent.
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 two sentences with no filler: it defines the ranking basis, gives an anchor example, and states the main usage. The key information is front-loaded.
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 three optional parameters and no output schema, the description covers what is returned, how it is ordered, and how to scope by country or category. Return-field details are omitted, but the input schema plus this description are sufficient for correct invocation.
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 coverage is 67%, with limit and country documented. The description adds value by clarifying that country and category can be combined as filters, but limit behavior is left to the schema and category options come only from the enum. This meets the baseline without going beyond it.
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 resource and behavior: a ranked list of the most famous castle landmarks worldwide or by country. It distinguishes itself from siblings like search_castles and random_castle by presenting itself as 'the direct answer to most famous castles in <country>'.
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?
It gives concrete usage context: use for worldwide or country-scoped fame rankings, and filter by country and/or category. It does not explicitly list alternatives or exclusions, but the 'direct answer' phrasing provides enough guidance to select this tool over its siblings.
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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