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Cta Train Arrivals

cta_train_arrivals
Read-onlyIdempotent

Real-time Chicago CTA 'L' train arrivals at a station — answers "when is the next train Chicago", next Red Line at Belmont, Blue Line to O'Hare from Clark/Lake. Returns each upcoming train's line color, destination, arrival time and minutes_away, plus approaching / delayed / scheduled-only flags and the platform description. Station accepts a name ("Belmont", "Clark/Lake", "O'Hare") or a 5-digit mapid (e.g. 41320). Example: cta_train_arrivals({ station: "Belmont", route: "Red" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
maxNoMax arrivals to return, 1-20 (default 8)
routeNoOptional 'L' line filter: Red, Blue, Brown, Green, Orange, Purple, Pink, or Yellow (also disambiguates same-named stations)
_apiKeyNoOptional: your own CTA credentials as "train_key:bus_key" — Train Tracker key (free at transitchicago.com/developers) + Bus Tracker key (free at ctabustracker.com)
stationYesStation name (e.g. "Belmont", "Clark/Lake", "O'Hare", "Midway") or 5-digit mapid (e.g. "41320")

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "_apiKey": "your-cta-api-key",
-    "route": "Red",
-    "station": "Belmont"
-  },
-  {
-    "_apiKey": "your-cta-api-key",
-    "max": 5,
-    "station": "Clark/Lake"
-  }
-]New value: +[
+  {
+    "route": "Red",
+    "station": "Belmont"
+  },
+  {
+    "max": 5,
+    "station": "Clark/Lake"
+  }
+]
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-cta-api-key",
+    "route": "Red",
+    "station": "Belmont"
+  },
+  {
+    "_apiKey": "your-cta-api-key",
+    "max": 5,
+    "station": "Clark/Lake"
+  }
+]
  3. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, open-world, and non-destructive behavior. The description adds useful behavioral context by stating the data is real-time and detailing the returned fields, including approaching/delayed/scheduled-only flags and platform description, which is valuable since there is no output schema.

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 dense but every sentence earns its place: purpose, answerable query forms, return fields, accepted station identifiers, and a concrete example. It is front-loaded with the core purpose and contains no filler.

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, low-complexity tool with a fully documented input schema, the description covers the essential behavior, accepted inputs, and return fields. It could additionally clarify ambiguous station name handling or error cases, but nothing critical is missing for correct invocation.

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?

Schema description coverage is 100%, so the schema already documents all four parameters including station, route, max, and _apiKey. The description adds an invocation example and common station names, but does not materially extend parameter meaning beyond the schema.

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 names a specific verb and resource ('returns real-time Chicago CTA 'L' train arrivals at a station'), and immediately distinguishes itself from related sibling tools like cta_train_positions by focusing on arrivals at a station. Example queries make the intended use unmistakable.

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 establishes clear context: this is the tool for station-based arrival predictions, contrasted implicitly with cta_train_positions and the CTA bus tools. It does not explicitly name alternatives or say when not to use it, so it stops short of a 5.

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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