Places worth a detour, from Wikidata: search, nearby lookup, fame rankings, stats. No key, CC0.
Average 4.3/5 across 8 of 8 tools scored. Lowest: 3.5/5.
Each tool targets a distinct query type: single record by slug, aggregate stats, country listing, kind listing, radius search, random selection, name search, and ranked top list. No two tools answer the same question.
Mix of verb-noun (get_place, list_countries, search_places) and noun-phrase tools (random_place, top_places, places_near). 'places_near' breaks the pattern, but all are snake_case and readable.
8 tools is well-scoped for a read-only places atlas; each tool serves a distinct user need with no redundancy.
Covers all expected read-only operations: lookup by identifier, search by name, spatial query, ranking, random, aggregates, and browse by country/kind. No missing lifecycle operations since the domain is read-only.
get_placeGet one place in fullAInspectFetch one place’s full record by slug (preferred, e.g. "neuschwanstein-castle") or exact name: coordinates, founding year, worldwide fame rank and tier, short description, photo URL, Wikipedia and Wikidata links, and a deep link that opens it on the map. Unsure of the slug? Call search_places first.
| Name | Required | Description | Default |
|---|---|---|---|
| place | Yes | Slug (preferred, e.g. "colosseum") or exact name |
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 what data is returned (coordinates, founding year, fame rank, etc.) and that it's a read operation. However, it does not mention permissions, rate limits, or potential errors.
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 long with no wasted words. It front-loads the verb and resource, then lists key fields efficiently.
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 tool with one parameter and no output schema, the description is complete. It lists all return fields and usage guidance. The absence of an output schema is compensated by the detailed list of returned data.
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%, but the description adds significant value: it specifies that slugs are preferred, provides an example ('neuschwanstein-castle'), and clarifies that exact names also work. This goes beyond the schema's simple 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 clearly states the tool's purpose: 'Fetch one place’s full record by slug or exact name'. It lists the specific fields returned (coordinates, founding year, etc.) and distinguishes from the sibling tool search_places.
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 recommends using a slug and provides guidance: 'Unsure of the slug? Call search_places first.' This tells the agent when to use this tool versus an alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_statisticsAtlas statisticsAInspectHeadline statistics computed live from the atlas: totals by kind, top countries, fame-tier sizes, dated-entry stats, geographic extremes and the most famous place. The source for aggregate claims like "how many castles are on Detourmap" or "which country has the most notable places".
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 states the tool is 'computed live' and lists return values, implying a read-only operation. It does not disclose rate limits or performance characteristics, but the information given is sufficient for typical use.
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 long, front-loading the key idea ('Headline statistics computed live'), and every word adds value. No redundancy or verbosity.
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?
Despite lacking an output schema, the description comprehensively lists the types of statistics returned (totals, top countries, fame tiers, etc.) and gives example questions. This is complete for a no-parameter, read-only 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?
With zero parameters and 100% schema coverage (by default), the baseline is 4. The description adds no parameter info because there are none, but that 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 is specific and informative: it states the tool computes 'headline statistics' from the atlas, listing categories like totals by kind, top countries, etc. It provides examples of questions it answers, clearly distinguishing it from siblings that handle individual places, lists, or spatial 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 implies usage for aggregate claims ('how many castles') but does not explicitly state when to use this tool over siblings or when not to use it. No direct comparison or exclusion criteria are provided, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_countriesCountries with place countsAInspectEvery country in the atlas with its place count, most first — answers "which country has the most notable places". Countries with eight or more places include a browsable index page 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 the full burden. It describes output structure (place count, URL for high counts) but does not disclose behaviors like read-only nature, rate limits, or authentication requirements.
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 with no wasted words, front-loaded with purpose.
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 and no annotations, the description provides a complete overview of what the tool returns, including key details like ordering and URL for high-count countries.
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?
No parameters exist, and schema coverage is 100%. The description adds meaning about the output content, which is valuable beyond the empty 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 clearly states it lists every country with its place count, sorted most first, and answers a specific question. It distinguishes itself from siblings like get_place and list_kinds.
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 context by answering a specific question, but it does not explicitly state when not to use or provide alternatives beyond the implied scope.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_kindsThe kinds of place on the mapAInspectThe layers Detourmap carries — archaeological and nature — with worldwide counts and a browsable index page for each. The atlas is cut to these kinds and holds nothing else.
| 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 the full burden. It does add useful behavioral context: the kinds are limited to archaeological and nature, counts are worldwide, and each kind has a browsable index page. However, it does not disclose the response shape, whether results are limited to these two kinds only, or any other operational details.
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 only, front-loaded with the core idea. The first sentence names the kinds and provides counts/index info; the second reinforces scope. No filler or 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 list tool, the description is largely sufficient: it names the two kinds, says counts are worldwide, and notes the atlas contains nothing else. Minor ambiguity remains about what a 'browsable index page' actually returns, but overall the tool is adequately described.
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 tool has zero parameters, and the schema is fully covered at 100% (empty object). The description compensates by explaining what the unparameterized call returns — all kinds with counts and index pages — which adds meaning 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 clearly states that the tool exposes the kinds of places on Detourmap — specifically archaeological and nature — along with worldwide counts and a browsable index page. This distinguishes it from sibling tools like list_countries and search_places, though the verb is implicit rather than explicit.
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 does not provide explicit when-to-use guidance or contrast with alternatives. It merely describes what the tool offers, leaving the agent to infer that this is the right tool for listing place categories. No exclusions or sibling comparisons are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
places_nearFind places worth a detour near a pointAInspectList notable places within a radius of a WGS84 coordinate, most famous first (rank order), each with distance_km — the direct answer to "what is worth visiting near ?". Radius defaults to 50 km (max 500). Geocode the location yourself, then call this with its latitude/longitude. Filter by kind to answer e.g. "waterfalls near Zurich".
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | Kind of place — key or slug: ruins (Archaeological), natural (Nature) (optional) | |
| limit | No | Max results (default 15) | |
| latitude | Yes | ||
| longitude | Yes | ||
| radius_km | No | Search radius in km (default 50) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It discloses output ordering ('most famous first, rank order'), the inclusion of distance_km, and radius limits (default 50, max 500). This provides meaningful behavioral insight beyond the schema.
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 concise with four short sentences, each adding essential information: core function, radius defaults, geocoding prerequisite, and filtering example. No fluff 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 or annotations, the description covers the tool's purpose, input format, ordering, distance metric, and filtering. It lacks a full return-field list, but the mention of distance_km and rank order gives sufficient expectation for a list 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?
Schema covers kind, limit, and radius_km with descriptions, but latitude/longitude only have bounds. The description compensates by explaining WGS84 coordinates and instructing to geocode first, and clarifies radius defaults. It also illustrates kind usage with the waterfall example, adding value over the schema's cryptic note.
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 notable places within a radius, ordered by fame, with distance_km. This distinguishes it from siblings like get_place (single place) or search_places (likely query-based).
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 explicitly says to geocode the location yourself, providing a prerequisite. It also gives a concrete example 'waterfalls near Zurich' and explains that filtering by kind answers such queries. It doesn't explicitly name alternatives but the context implies it's for coordinate-based discovery.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
random_placeA random place worth a detourAInspectOne random place with its full record — deliberately biased toward the little-known long tail, like the site’s own "Surprise me" button. Optionally limit by country and/or kind. Great for discovery and "place of the day" features.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | Kind of place — key or slug: ruins (Archaeological), natural (Nature) (optional) | |
| country | No | Country name or ISO code (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral transparency burden. It discloses the deliberate bias toward the 'little-known long tail' and that it returns the 'full record', which is valuable beyond the obvious random selection. It does not mention potential duplicates or the exact randomness distribution, but these are minor for a read-only discovery 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?
The description is two sentences long, front-loaded with the core action, and every sentence contributes unique value: the first defines the tool, the second explains filters and use cases. 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 the simple schema (2 optional parameters, no output schema or annotations), the description is nearly complete. It covers purpose, bias, filters, and use cases. A minor gap is the lack of explicit return format, but 'full record' implies the response, making the tool adequately specified.
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 already describes both parameters (kind and country) with examples, achieving 100% coverage. The description merely repeats that these are optional limiters, adding no semantic meaning beyond the schema, so the baseline score of 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?
The description clearly states the tool returns 'one random place with its full record', which is a specific verb+resource. It distinguishes from siblings like search_places and get_place by emphasizing randomness and the 'long tail' bias, making the unique purpose unmistakable.
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 recommends the tool for discovery and 'place of the day' features, and notes optional filtering by country/kind. It does not explicitly mention when not to use it or name alternatives, but the context is clear enough for an AI agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_placesSearch places by nameAInspectSearch 39,832 notable places worldwide by name (accent- and case-insensitive substring match): archaeological and nature. Optionally filter by country (name or ISO code) and kind. Results come best-match-first, then by fame; each carries coordinates, fame rank/tier and links to the map and Wikipedia. If nothing matches, retry with a shorter fragment.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | Kind of place — key or slug: ruins (Archaeological), natural (Nature) (optional) | |
| limit | No | Max results (default 10) | |
| query | Yes | Name or part of a name, e.g. "pyramiden" or "plitvice" | |
| country | No | Country name or 2-letter ISO code (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses matching behavior (accent/case-insensitive substring), result ordering (best-match-first, then fame), and output contents (coordinates, fame rank/tier, links to map and Wikipedia). The retry tip also hints at failure behavior. It does not mention authorisation or rate limits, but for a read-only search that is less critical. This is strong behavioral disclosure, though not exhaustive.
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, front-loaded with core purpose and scope, then details filters and result format, and ends with an actionable tip. Every clause earns its place; no redundancy or filler. Structure clearly guides the reader through what, how, and what to expect.
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 search tool with four parameters, no output schema, and no annotations, this description is quite complete. It covers the matching mechanism, filters, result ordering, output contents, and a failure-retry hint. It does not describe pagination or the exact JSON structure, but the 'limit' parameter is covered in the schema, and the description's result summary is sufficient for an agent to invoke it correctly. A 5 would require explicit coverage of pagination or return envelope details.
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%, giving a baseline of 3. The description adds extra semantics beyond the schema: it specifies that the match is accent- and case-insensitive (not just a plain substring), clarifies that country can be a name or ISO code (schema also mentions this, but description reinforces), and explains the 'kind' values in plain terms. This enriches parameter understanding beyond the schema fields.
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 states a specific verb ('search') and resource ('39,832 notable places worldwide by name') with detailed matching semantics (accent/case-insensitive substring). It also differentiates from siblings like get_place, places_near, and list_countries by clarifying that this searches by name across all places, rather than fetching a specific place, searching by location, or listing reference data.
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?
Provides clear context on when to use: when you have a name fragment to find places. It mentions optional filters (country, kind) and offers an explicit tip ('if nothing matches, retry with a shorter fragment'). However, it does not explicitly exclude alternatives (e.g., using places_near for location-based search or get_place for a known ID), so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
top_placesMost famous placesAInspectThe most famous places worldwide, in one country, or of one kind — ordered by Detourmap fame rank (derived from Wikipedia language coverage; rank 1 is the most famous). The direct answer to "most famous in " and "what are the must-sees in ".
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | Kind of place — key or slug: ruins (Archaeological), natural (Nature) (optional) | |
| limit | No | How many (default 10) | |
| country | No | Country name or ISO code (optional — omit for worldwide) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the ordering logic (Derived from Wikipedia language coverage, rank 1 is most famous), which is a key behavioral trait. It also clarifies the filtering scope (worldwide, by country, by kind). It does not mention response format or error behavior, but the core semantics are 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 only two sentences, with zero fluff. The first sentence delivers the core purpose and ranking logic; the second provides concrete query examples. It is front-loaded and every word earns its place.
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 (three optional parameters, no output schema), the description covers the essential functionality and use cases. It could mention the return format or behavior when no results are found, but the description is sufficiently complete for an AI agent to understand when and how to invoke it.
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 already provides 100% parameter coverage with useful descriptions for kind, limit, and country. The description adds no extra parameter-level detail beyond reiterating the kinds and the optional nature (which the schema already includes). Since the schema does the heavy lifting, a 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 'the most famous places worldwide, in one country, or of one kind' with a specific ranking by Detourmap fame rank. It uses a concrete resource and distinguishes itself from sibling tools like places_near (nearby) and search_places (keyword search) by emphasizing the fame-based ordering and the three filtering dimensions.
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 frames the tool as the direct answer to specific natural-language queries like 'most famous <kind> in <country>' and 'what are the must-sees in <country>', giving strong usage context. It does not name alternatives or provide when-not-to-use guidance, but the use cases are clear enough to guide selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
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The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
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For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
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Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
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