Castles, fortresses, palaces and ruins worldwide: search, nearby, fame ranking, statistics. CC0.
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" (Spain 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?
With no annotations, the description carries full burden for behavioral transparency. It discloses that results are ordered most first, each row contains country name and ISO code, and country pages are accessible via the countries_index URL. This gives useful behavioral context beyond a bare 'list countries' statement, though it does not cover pagination or data freshness.
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 sentence that front-loads the core action and result, then efficiently adds ordering, the example answer (Spain), and output details. Every clause earns its place, with no wasted words or redundant schema repetition.
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 zero-parameter tool with no output schema, the description is complete: it explains what is returned (country name and ISO code), how it is ordered (most first), what question it answers (most castles), and how to navigate further (countries_index URL). No significant gaps remain for an agent to invoke this 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?
The input schema has zero parameters, so the baseline is 4. The description adds no parameter-level detail because none exists, and the no-parameter nature is consistent with a tool that simply returns all countries. No additional semantic guidance is needed.
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 in the atlas with its landmark count, sorted most first, and explicitly answers the question 'which country has the most castles' (Spain leads). This distinguishes it from sibling tools like get_castle or search_castles, which focus on 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 provides clear context for when to use this tool: when you need a complete country-level overview with counts, especially to identify which country has the most castles. It does not explicitly mention alternatives or exclusion criteria, but the purpose is unambiguous enough to guide selection.
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 8,133 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 full burden. It discloses caseless and accent-insensitive matching, filter capability, exact sort ordering, returned fields, and no-match retry guidance. Minor omissions remain around response format and error behavior, but overall the behavioral profile is well covered.
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 with no filler. The core search behavior and matching rule are front-loaded; sorting, result fields, and retry guidance follow in a natural order. Every clause adds value.
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-oriented search tool with four parameters and no output schema, the description covers matching, filtering, ordering, returned fields, and fallback behavior. It is complete enough for an agent to invoke the tool correctly without opening schemas or inspecting siblings.
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%, so the baseline is 3. The description adds extra meaning by clarifying accent/case-insensitive substring semantics, country-by-name-or-ISO-code behavior, and the value of using shorter query fragments. That pushes it above 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?
The description states a specific search-by-name behavior across a well-defined corpus ('8,133 castles, fortresses, palaces and ruins') and contrasts itself with sibling tools like castles_near, top_castles, and get_castle. The verb and match semantics are precise and non-tautological.
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 clearly establishes when to use the tool: when you need a name-based substring search, optionally filtered by country or category. It also gives concrete advice to retry with a shorter fragment if nothing matches. However, it does not explicitly name when to choose a sibling tool, such as castles_near or top_castles.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
No tool schema history has been recorded yet.
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Places worth a detour, from Wikidata: search, nearby lookup, fame rankings, stats. No key, CC0.
Search 10,500 French castles and read verified entries: hours, prices, accessibility.
Where films and TV were shot, and where games, anime and manga are set. Wikidata, read only, CC0.
Read-only Wheel of Heaven corpus: myth search, primary texts, datasets, typed graph. CC0.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: geospatial search (castles_near), single-record retrieval (get_castle), aggregate statistics (get_statistics), country overviews (list_countries), random discovery (random_castle), name-based search (search_castles), and fame-based rankings (top_castles). The only slight overlap between list_countries and get_statistics is minimal and both descriptions clearly separate them.
All tool names use lowercase snake_case and are self-explanatory, but they mix verb-first patterns (get_, list_, search_) with adjective or noun-first patterns (random_castle, top_castles, castles_near). This is a minor deviation from a fully consistent verb_noun convention, though the inconsistency does not hinder readability.
With 7 tools, the set is well-scoped for a castle atlas. Each tool covers a distinct need—search, get, near, top, random, stats, countries—without redundancy or bloat.
The toolset covers most read-oriented workflows: finding, retrieving, ranking, and statistics. A notable gap is the lack of a way to list all castles in a country or category without a name search term; search_castles requires a name substring and top_castles returns only the most famous. This is a workaround by using search with a broad term, but it's not a first-class operation.
