Our own written piece on a resort — what the mountain is like, who it suits, what it does badly — in the reader's language where we have it.
673 resorts have one. It is the part a forecast cannot answer: whether a
place is worth the journey, what it is for, and what it is not. Use it when
somebody asks "what is X like?" rather than "how much snow".| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | en | |
| resort | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context: it is a human-written editorial piece, available for 673 resorts, and localized 'in the reader's language where we have it'. This goes beyond the annotations and helps the agent set expectations about content type and coverage.
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 compact and front-loaded. The first sentence states the core purpose, the second adds coverage and context, and the third gives a clear usage rule. Every sentence earns its place with no 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?
For a simple read-only tool with 2 parameters and no output schema, the description is largely complete. It explains what the content is, when to use it, and its coverage. The only minor gap is not explicitly stating what the return value looks like (e.g., a text block), but the description's characterization of it as a 'written piece' makes that reasonably clear.
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 0%, so the description must compensate. It explains the 'resort' parameter implicitly (the write-up is about a resort) and mentions 'lang' indirectly via 'in the reader's language where we have it'. However, it doesn't explicitly state that 'lang' controls the language of the write-up or that 'resort' is the resort identifier. The description adds some meaning but leaves the parameter mapping somewhat implicit.
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 what the tool does: it returns a written editorial piece about a resort, covering what the mountain is like, who it suits, and what it does badly. It also distinguishes itself from forecast tools by explicitly saying it answers 'what is X like?' rather than 'how much snow'.
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 tells the agent when to use this tool: when someone asks 'what is X like?' rather than 'how much snow'. It also contrasts with forecast tools, which are siblings like snow_forecast and conditions, making the routing decision clear.
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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