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Glama

list_trains

Retrieve a full listing of all trains on your surface—their id, state, mode, cars, position, station, schedule, and cargo—along with known train stop names.

Instructions

Trains on your surface (id, state, mode, cars, position, station, schedule, cargo) and the known train stop names.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It communicates a read-only listing scoped to the current surface and notes that train stop names are included, but it does not explicitly state that no mutation occurs or describe the result format. For a simple list tool this is adequate but not deeply transparent.

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?

The description is a single compact sentence with a dense field list and no filler. The telegraphic phrasing is slightly awkward, but every word contributes useful information.

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 zero-parameter schema and the existence of an output schema, the description is mostly complete: it states the surface scope and the fields returned. It lacks a when-to-use note, but the agent can likely invoke it correctly without more information.

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 zero properties, so there are no parameter semantics to document. The description adds no parameter information, which is appropriate for a zero-parameter tool.

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?

The description identifies the resource ('trains'), the scope ('on your surface'), and the exact fields returned, so the purpose is clear despite missing an explicit verb. It does not explicitly differentiate from related siblings like set_train_schedule, but the name 'list_trains' plus the field list make the operation unambiguous.

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?

There is no guidance about when to use this tool versus alternatives such as set_train_schedule or status. The intended use case must be inferred entirely from the tool name and the enumerated fields.

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