Forecast peak autumn-foliage dates for viewing spots across Japan. Free tier, no key.
Fetch the full forecast document for one foliage spot by id (the slug returned by search or find_peak_spots, e.g. 'tenryuji-temple'): peak window, confidence, status, nearest rail station, leaf type, and page URL.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Spot id (slug) from search. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish that this is read-only, idempotent, and non-destructive. The description adds useful behavioral detail by specifying what the returned forecast document contains, which is valuable since there is no output schema. There is no contradiction with 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 entire description is a single, front-loaded sentence with no filler. It states the action, resource, input source, and expected output contents 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 one-parameter read operation with strong annotations, the description is complete: it specifies how to obtain the id, what will be returned, and the nature of the operation. No critical information 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 coverage is 100% and the schema already explains that id is a spot slug from search. The description reinforces this and adds a concrete example ('tenryuji-temple'), but it does not add substantial 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 states a specific verb ('Fetch'), a clear resource ('full forecast document for one foliage spot by id'), and enumerates the document contents. It also includes a concrete slug example, making the tool's purpose unmistakable and distinct from sibling search/find tools.
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 the input should come from search or find_peak_spots, which tells the agent when fetch is appropriate: after a spot has been identified. It does not explicitly state when not to use it relative to get_coverage, but the single-spot-by-id framing provides sufficient context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find Japan autumn-foliage viewing spots whose forecast best-viewing window covers a date or date range, optionally near a place. Use it for 'where should I go on ?', 'when does peak?', and 'is still at peak on ?' questions. Give a date (or date_from/date_to) plus optionally a location: near (city or prefecture name, e.g. 'Kyoto') or lat/lon, with radius_km; or restrict by region or prefecture. Returns each spot's forecast peak window (start–end), confidence in days, nearest rail station, leaf type, and a page URL, sorted by prominence then proximity. Example prompts: 'Which foliage spots near Kyoto peak on 20 November?', 'Best koyo in Tohoku the first week of November', 'Is Nikko past peak by 1 December?'. Example call: {date: '2026-11-20', near: 'Kyoto', radius_km: 60}.
| Name | Required | Description | Default |
|---|---|---|---|
| lat | No | Latitude, alternative to near. | |
| lon | No | Longitude, alternative to near. | |
| date | No | Target date, YYYY-MM-DD. Use this OR date_from/date_to. | |
| near | No | City or prefecture name to search around, e.g. 'Kyoto', 'Nikko', 'Aomori'. | |
| limit | No | Max results, default 10, max 25. | |
| region | No | Restrict to one region of Japan. | |
| status | No | 'peak' (default): spots whose best-viewing window covers the date(s). 'any': no status filtering, statuses labeled. | |
| date_to | No | End of a date range, YYYY-MM-DD (pair with date_from). | |
| date_from | No | Start of a date range, YYYY-MM-DD (pair with date_to). | |
| radius_km | No | Search radius around the location in km. Default 60 (day-trip range). | |
| prefecture | No | Restrict to one prefecture, e.g. 'Aomori'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds meaningful behavioral context: returned fields (peak window start–end, confidence in days, nearest rail station, leaf type, page URL) and sort order (prominence then proximity). This goes beyond the annotations without contradicting them.
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 dense but every sentence earns its place: core behavior, question types, parameter guidance, return format, examples. Key scoping information is front-loaded, and examples at the end reinforce the rules without adding 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?
With 11 parameters, no required fields, no output schema, and three sibling tools, the description still covers the essential call patterns, parameter combinations, return payload, and sorting behavior. The example prompts and example call give an agent enough to select and invoke this tool correctly in varied user scenarios.
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 relational meaning: date OR date_from/date_to, near OR lat/lon, radius_km as an optional modifier, and region/prefecture as restrictors. The example call {date: '2026-11-20', near: 'Kyoto', radius_km: 60} demonstrates valid parameter combination, which the flat schema alone does not 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 and resource: 'Find Japan autumn-foliage viewing spots' whose forecast window covers a date/range. The three listed question types ('where should I go on <date>?', 'when does <place> peak?', 'is <spot> still at peak on <date>?') make the tool's specialized purpose unmistakable and distinguish it from generic siblings like search or fetch.
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 'Use it for...' and provides concrete example prompts and an example call, giving clear context on when to invoke the tool. It does not explicitly state when not to use it or name alternatives, 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.
Describe what the RoamTime Japan autumn-foliage (koyo) forecast covers: forecast year and issue date, season span, regions and prefectures, spot count, and the access tier (free tier or full dataset). Call this first to orient yourself, to check whether a place or date is in scope, and to learn the as_of date to cite. Takes no arguments. Example prompts: 'What does the RoamTime foliage forecast cover?', 'Is Hokkaido included?'
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark it read-only, idempotent, and non-destructive. The description adds the expected disclosure content—what fields are returned, that it takes no arguments, and that it yields a citable as_of date—so the agent knows what behavior to expect beyond the safety hints.
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 and two example prompts with no filler. The scoping information is front-loaded, usage guidance comes next, and examples close it out; every clause 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?
For a zero-argument metadata tool with no output schema, the description lists all relevant output dimensions, states when to call it, and shows example invocations. An agent can select and interpret this tool without further information.
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; the schema is empty and coverage is 100%. The description explicitly confirms 'Takes no arguments', satisfying the baseline for a zero-parameter tool.
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 'Describe what the RoamTime Japan autumn-foliage forecast covers'—a specific verb and resource—and enumerates the exact metadata fields (forecast year, issue date, season span, regions/prefectures, spot count, access tier). This clearly distinguishes it as the coverage/orientation tool from siblings like find_peak_spots and search.
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 instructs agents to 'Call this first to orient yourself' and gives concrete trigger conditions: check whether a place/date is in scope and learn the as_of date to cite. It doesn't name exclusions or alternatives, but the 'first' guidance and scope-checking purpose are clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Japan autumn-foliage viewing spots by free text: a spot name (English or Japanese), a prefecture, a region, or a month such as 'November'. Returns matching spot ids, titles, and page URLs; pass an id to fetch for the full forecast. Example queries: 'Kyoto', 'Nikko', '奥入瀬', 'Tohoku November'.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Free-text search query. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds value by clarifying the search scope (Japan foliage spots) and the return behavior (matching ids, titles, URLs), but it does not disclose pagination, result limits, or how partial matches are ranked. With annotations covering the key behavioral traits, a 3 is appropriate – the description adds some context but not deep behavioral detail.
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 that front-loads the core purpose before giving examples. It is concise, with no filler or repetition of schema fields. However, the example list and the return-value detail could be slightly tightened without loss, which prevents a 5.
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 single-parameter, read-only search tool with no output schema, the description is quite complete: it covers input semantics, return values, and the next-step workflow (fetch with an id). It does not specify result count limits or ordering, but given the tool's simplicity and annotation coverage, these are minor gaps. An agent can invoke this tool correctly with confidence.
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% for the single parameter (query described as 'Free-text search query'), so the schema already documents the parameter. The description adds meaning by detailing what kinds of free text are valid (names, prefectures, regions, months) and providing examples, which supports but does not fully compensate for the schema's minimal description. Baseline 3 is appropriate since the description enriches but does not dramatically extend 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 states a specific verb ('Search'), a clear resource ('Japan autumn-foliage viewing spots'), and the exact input type (free text matching spot names, prefectures, regions, or months). It also distinguishes itself from siblings by specifying what it returns (spot ids, titles, page URLs) and how to proceed to full details via fetch, so an agent can tell it apart from sibling tools like get_coverage or fetch.
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 when to use this tool ('by free text') and what kinds of queries are appropriate ('a spot name, a prefecture, a region, or a month such as November'). It also gives example queries and states the follow-up action ('pass an id to fetch for the full forecast'), implying when a sibling tool like fetch is needed instead. This provides clear context without being wordy.
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.
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Each tool has a distinct role: get_coverage for scope, search for free-text lookup, find_peak_spots for date/location queries, and fetch for detail by id. There is some overlap between search and find_peak_spots and between find_peak_spots and fetch, but the descriptions are specific enough to route correctly.
All names are lowercase imperative verbs, which provides a consistent style, but fetch and search are bare verbs while find_peak_spots and get_coverage use verb_noun construction. This is a minor structural inconsistency rather than a chaotic mix.
With four tools covering one narrow forecast domain, the set is appropriately scoped. No tool feels extraneous, and there are enough tools to answer the main forecast questions without overwhelming the agent.
The surface covers orientation via get_coverage, discovery by text or date/location via search and find_peak_spots, and full detail retrieval via fetch. For a read-only forecast server this is complete; no obvious missing operation blocks a typical query workflow.
