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SBB Open Data MCP Server

by malkreide
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

SBB Perrondaten

sbb_get_platform_data
Read-onlyIdempotent

Retrieve platform length, area, type, and accessibility details for SBB stations, filtering by station name or platform type.

Instructions

Ruft Perrondaten (Länge, Fläche, Typ) für SBB-Bahnhöfe ab.

Enthält Perronlänge (m), Netto-/Bruttofläche (m²), Perrontyp und Angaben zur schienenfreien Zugänglichkeit.

Args: params (PlatformDataInput): Parameter: - station_name (Optional[str]): Bahnhofsname, z.B. 'Zürich HB' - platform_type (Optional[str]): Perrontyp, z.B. 'Mittelperron' - limit (int): Max. Resultate - offset (int): Paginierung - response_format (str): 'markdown' oder 'json'

Returns: str: Perrondaten mit Länge, Fläche und Typ. Schema: {station, platform_nr, type, length_m, gross_area_m2, net_area_m2, accessible}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsYesRecords as returned by data.sbb.ch
paginationYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.4.0
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$defs": {
+    "PaginationOutput": {
+      "properties": {
+        "has_more": {
+          "title": "Has More",
+          "type": "boolean"
+        },
+        "next_offset": {
+          "anyOf": [
+            {
+              "type": "integer"
+            },
+            {
+              "type": "null"
+            }
+          ],
+          "title": "Next Offset"
+        },
+        "offset": {
+          "title": "Offset",
+          "type": "integer"
+        },
+        "returned": {
+          "title": "Returned",
+          "type": "integer"
+        },
+        "total_count": {
+          "title": "Total Count",
+          "type": "integer"
+        }
+      },
+      "required": [
+        "total_count",
+        "returned",
+        "offset",
+        "has_more",
+        "next_offset"
+      ],
+      "title": "PaginationOutput",
+      "type": "object"
+    }
+  },
+  "description": "A page of raw data.sbb.ch records plus pagination.",
+  "properties": {
+    "pagination": {
+      "$ref": "#/$defs/PaginationOutput"
+    },
+    "results": {
+      "description": "Records as returned by data.sbb.ch",
+      "items": {
+        "additionalProperties": true,
+        "type": "object"
+      },
+      "title": "Results",
+      "type": "array"
+    }
+  },
+  "required": [
+    "pagination",
+    "results"
+  ],
+  "title": "PagedRecordsOutput",
+  "type": "object"
+}
  2. Changed1 schema field changedv0.3.4
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "result": {
-      "title": "Result",
-      "type": "string"
-    }
-  },
-  "required": [
-    "result"
-  ],
-  "title": "sbb_get_platform_dataOutput",
-  "type": "object"
-}New value: +null
  3. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is fully covered. The description adds that results can be paginated (limit/offset) and returned as markdown or json, but discloses no rate limits, auth requirements, or data-freshness caveats beyond 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.

Conciseness3/5

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

Purpose is front-loaded and the Args block is useful given the low schema coverage, but the Returns section repeats a field schema that an output schema already defines, which is redundant. Structure is clear (purpose / args / returns) but not maximally economical.

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

Completeness4/5

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

For a read-only tool with rich annotations and an existing output schema, the definition covers purpose and parameters adequately. The only substantive gap is the absence of usage guidance versus sibling tools, which the annotations cannot supply.

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 description coverage is reported at 0%, so the description carries the burden and largely does: it glosses station_name, platform_type, limit, offset, and response_format, and even supplies enum values ('markdown' oder 'json') and examples ('Zürich HB'). It slightly duplicates schema examples, but meaningfully compensates for the documentation gap.

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

Purpose4/5

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

States a specific verb and resource: 'Ruft Perrondaten (Länge, Fläche, Typ) für SBB-Bahnhöfe ab', and enumerates what the data contains (length, net/gross area, type, barrier-free access). Clear enough to distinguish from siblings like passenger frequency or rolling stock, though it never names an alternative explicitly.

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

Usage Guidelines2/5

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

The description says what the tool returns but offers no when-to-use guidance, no prerequisites, and no mention of alternatives among the eight sibling tools. The agent must infer the usage context entirely.

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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