Skip to main content
Glama

get_station_number

Find the station number for any Nagoya bus stop by name, using fuzzy matching to handle typos or partial names.

Instructions

Get station number for a given station name using fuzzy matching.

Attempts exact match first, then falls back to fuzzy matching with 60% similarity threshold if no exact match is found.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
station_nameYesThe station name to look up (e.g., "名古屋駅").

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
station_nameYesバス停名
station_numberYesバス停番号

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed11 schema fields changedv1.0.1
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / station_name / description
      Added value: +"The station name to look up (e.g., \"名古屋駅\")."
    • removedInput schema / properties / station_name / title
      Removed value: -"Station Name"
    • removedOutput schema / $defs
      Removed value: -{
      -  "StationNumberResponse": {
      -    "properties": {
      -      "station_name": {
      -        "anyOf": [
      -          {
      -            "type": "string"
      -          },
      -          {
      -            "type": "null"
      -          }
      -        ],
      -        "default": null,
      -        "title": "Station Name"
      -      },
      -      "station_number": {
      -        "anyOf": [
      -          {
      -            "type": "integer"
      -          },
      -          {
      -            "type": "null"
      -          }
      -        ],
      -        "default": null,
      -        "title": "Station Number"
      -      },
      -      "success": {
      -        "title": "Success",
      -        "type": "boolean"
      -      }
      -    },
      -    "required": [
      -      "success"
      -    ],
      -    "title": "StationNumberResponse",
      -    "type": "object"
      -  }
      -}
    • addedOutput schema / description
      Added value: +"Response model for station number lookup by name.\n\nUsed by the get_station_number tool to return station lookup results,\nincluding fuzzy matching outcomes."
    • removedOutput schema / properties / result
      Removed value: -{
      -  "anyOf": [
      -    {
      -      "$ref": "#/$defs/StationNumberResponse"
      -    },
      -    {
      -      "type": "null"
      -    }
      -  ]
      -}
    • addedOutput schema / properties / station_name
      Added value: +{
      +  "description": "バス停名",
      +  "type": "string"
      +}
    • addedOutput schema / properties / station_number
      Added value: +{
      +  "description": "バス停番号",
      +  "type": "integer"
      +}
    • changedOutput schema / required
      Previous value: -[
      -  "result"
      -]New value: +[
      +  "station_name",
      +  "station_number"
      +]
    • removedOutput schema / title
      Removed value: -"_WrappedResult"
    • removedOutput schema / x-fastmcp-wrap-result
      Removed value: -true
  2. First observedv1.0.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the fuzzy matching behavior and the fallback from exact match, which is transparent. It does not mention error handling or read-only nature, but for a simple lookup, the disclosed behavior 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 very concise, consisting of two short sentences. The first sentence states the purpose and method, and the second elaborates on the algorithm. There are no superfluous words, and the information is front-loaded.

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?

Given the tool has only one parameter and an output schema (though not shown), the description is fairly complete. It explains the input, the algorithm, and the purpose. It does not mention the output format or error handling, but the output schema likely covers the return value. For its simplicity, it is adequate.

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?

The input schema has 100% coverage for the single parameter, with a description. The tool description adds details about the matching algorithm beyond the schema, providing context on how the parameter is used. This adds value beyond the schema's built-in description.

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 purpose: to get a station number from a station name using fuzzy matching. It specifies the verb 'get', the resource 'station number', and adds detail about the matching logic. It distinguishes itself from siblings by being a lookup tool, while siblings are for routes and timetables.

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?

The description explains when to use the tool: when you have a station name and need its number. It provides context about the matching algorithm (exact then fuzzy with 60% threshold), which guides usage. However, it does not explicitly state when not to use or mention alternatives, but the sibling tools are clearly different, so this is acceptable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.