rail_predictionsFetch real-time next-train arrival predictions for one or more WMATA stations by comma-separated station_codes (or "All"). Returns line, destination, car count, and minutes until arrival.
| Name | Required | Description | Default |
|---|---|---|---|
| station_codes | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "station_codes": "A01"
+ },
+ {
+ "station_codes": "A01,C05"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Response from WMATA rail predictions API",
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint=false, so the tool is safe and side-effect-free. The description adds behavioral details: the return fields (line, destination, car count, minutes until arrival) and that 'All' is a valid input. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: the first states purpose and input format; the second lists return fields. No unnecessary words. Front-loaded with the key action and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given only one parameter, an output schema (though not shown), and annotations covering safety, the description fully explains input and output behavior. It is complete for a simple data fetch tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With only one parameter and 0% schema coverage, the description compensates by explaining the parameter: 'comma-separated station_codes (or "All")'. This adds meaning beyond the schema's type string and aids correct invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'Fetch', resource 'real-time next-train arrival predictions', and scope 'one or more WMATA stations by comma-separated station_codes (or "All")'. It distinguishes from sibling tools like bus_predictions and rail_incidents by specifying the exact data and input format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use this tool for train arrival predictions with a specific input format. It does not explicitly state when to avoid it or name alternatives, but the context of sibling tools (bus_predictions, rail_incidents) is clear enough for an agent to infer appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.