fetchFetch 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. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
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.
Add one secure layer between your agents and this server.
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.