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MeteoSwiss MCP Server

by io.github.eins78
OverviewDiscussionsAdmin

meteoswissStations

List and search MeteoSwiss measurement stations. Filter by name, canton, or browse the full network of ~300 stations (~160 full weather + ~140 precipitation-only) across Switzerland.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results (1-200, default 20)
cantonNoFilter by canton abbreviation (e.g., "ZH", "BE", "GR")
searchNoSearch by station name or abbreviation

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalYesTotal number of stations matching the filter
sourceYesData attribution
stationsYesMatching stations, up to `limit`

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / search / maxLength
      Added value: +200
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "source": {
+      "description": "Data attribution",
+      "type": "string"
+    },
+    "stations": {
+      "description": "Matching stations, up to `limit`",
+      "items": {
+        "additionalProperties": false,
+        "properties": {
+          "abbreviation": {
+            "description": "Official station abbreviation (e.g. \"SMA\")",
+            "type": "string"
+          },
+          "canton": {
+            "description": "Canton abbreviation (e.g. \"ZH\")",
+            "type": "string"
+          },
+          "coordinates": {
+            "additionalProperties": false,
+            "description": "WGS84 coordinates of the station",
+            "properties": {
+              "lat": {
+                "type": "number"
+              },
+              "lon": {
+                "type": "number"
+              }
+            },
+            "required": [
+              "lat",
+              "lon"
+            ],
+            "type": "object"
+          },
+          "data_since": {
+            "description": "Date the station started reporting data",
+            "type": "string"
+          },
+          "elevation": {
+            "description": "Elevation in metres above sea level",
+            "type": "number"
+          },
+          "name": {
+            "description": "Station name",
+            "type": "string"
+          }
+        },
+        "required": [
+          "abbreviation",
+          "name",
+          "canton",
+          "elevation",
+          "coordinates",
+          "data_since"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total": {
+      "description": "Total number of stations matching the filter",
+      "type": "number"
+    }
+  },
+  "required": [
+    "total",
+    "stations",
+    "source"
+  ],
+  "type": "object"
+}
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the behavioral transparency burden. It discloses the scope of the tool ('~300 stations across Switzerland') and breaks down station types ('~160 full weather + ~140 precipitation-only'), which adds useful context beyond a generic listing tool. It doesn't mention rate limits or side effects, but for a read-only catalog, this is sufficient.

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

Conciseness5/5

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

The description is two sentences: the first states the primary action and resource, the second adds network composition detail. No wasted words, and the most important information is front-loaded. It earns every word it uses.

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?

For a list/search tool with an output schema, the description covers the essential aspects: purpose, filtering options, and network scope. The output schema handles return format, and sibling names clarify this is the station reference tool. No significant gaps remain.

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

Parameters3/5

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

The input schema has 100% coverage with descriptions for all three parameters (limit, canton, search), including examples. The description reiterates the filtering concepts ('Filter by name, canton') but adds little beyond what the schema already captures. Baseline 3 is appropriate since the schema does the heavy lifting.

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?

The description clearly states the tool's function: 'List and search MeteoSwiss measurement stations.' It names the specific resource (MeteoSwiss stations) and the actions (list, search), and distinguishes it from sibling tools like meteoswissClimateData or meteoswissCurrentWeather by focusing on station metadata.

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?

It provides clear usage context: filter by name/canton or browse the full network. While it doesn't explicitly mention alternatives or when-not-to-use, the sibling tools are clearly for different data (weather, climate, pollen), so the station-focused purpose implicitly guides selection. The context is clear, but explicit exclusions are absent.

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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