PeakCam Snow

Find powder

find_powder

Read-onlyIdempotent

Rank resorts by new snow in the last 24 or 48 hours, optionally within one state/province and above a minimum. Pass sourceOnly="snotel" to answer "where did it snow most, measured only" with station data alone. Resorts with no report are left out; zero inches is a measurement. Each row also carries today's Snow Score (score, 0–100 with a rating) when one is stored; get_score explains it. Sources: snotel = measured at a USDA NRCS SNOTEL station near the resort (station elevation, not the summit), open_meteo = weather-model estimate for resorts without a station.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Rows to return, 1–25.
`state`	No	US state or Canadian province code (`CO`, `UT`, `BC`), or for the Andes the country name PeakCam lists (`Chile`, `Argentina`). Case-insensitive.
`window`	No	Which window ranks first; the other breaks ties.	24h
`minInches`	No	Keep only resorts with at least this many inches in the window.
`sourceOnly`	No	`snotel` keeps only station-measured reports and drops model estimates.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`count`	Yes
`window`	Yes
`filters`	Yes
`license`	Yes
`results`	Yes
`attribution`	Yes
`generatedAt`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 20, 2026

addedOutput schema / properties / results / items / properties / score

Added value: +{
+  "anyOf": [
+    {
+      "additionalProperties": false,
+      "properties": {
+        "date": {
+          "description": "YYYY-MM-DD.",
+          "type": "string"
+        },
+        "rating": {
+          "enum": [
+            "great",
+            "good",
+            "fair",
+            "poor"
+          ],
+          "type": "string"
+        },
+        "score": {
+          "maximum": 100,
+          "minimum": 0,
+          "type": "integer"
+        }
+      },
+      "required": [
+        "date",
+        "score",
+        "rating"
+      ],
+      "type": "object"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "description": "Today's Snow Score (P7), when the caller loaded the score index."
+}

First observedOct 18, 2026

TDQS

A4.6/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the read-only/idempotent safety profile, so the description focuses on semantics an agent cannot get from structured fields: resorts with no report are excluded, zero inches counts as a real measurement, each row carries a stored Snow Score, and the two sources differ (station-measured at station elevation vs model estimate for resorts lacking a station). That is exactly the kind of behavioral context that prevents misreading results.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads the core purpose in the first clause, then layers filtering semantics, then source definitions. Every sentence carries information, though the trailing source-definition sentence is dense and could be slightly tighter.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, return-shape detail is unnecessary, and the description still supplies the interpretation layer (score meaning, source provenance, exclusion of unreported resorts) that the schema and annotations cannot. Nothing an agent needs to call or read this tool correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3, but the description adds meaning beyond the schema: why sourceOnly exists (isolate station-measured answers) and how zero inches and missing reports should be interpreted. Window tie-breaking and state format are already in the schema, so this sits just above baseline rather than far above it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource with scope: rank resorts by new snow over a 24h/48h window, with optional state and minimum-inch filters. The 'powder' framing plus explicit window and filtering behavior distinguishes it from generic siblings like get_snow_history or get_leaderboard without needing to open a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete usage conditions: pass sourceOnly="snotel" to answer 'where did it snow most, measured only', and points to get_score for interpreting the score field. It does not explicitly route between this tool and similar siblings (get_snow_history, compare_resorts), but the context provided 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.

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