Skip to main content
Glama

Cta Bus Positions

cta_bus_positions
Read-onlyIdempotent

Live Chicago CTA bus positions on a route — every vehicle currently running: lat/lon, heading, destination, delayed flag, and distance along the pattern. Answers "where is the 22 Clark bus right now" via the CTA bus tracker. Example: cta_bus_positions({ route: "22" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
routeYesBus route number, e.g. "22", "66", "146". Comma-separable up to 10.
_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)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
      -  {
      -    "_apiKey": "your-cta-api-key",
      -    "route": "22"
      -  }
      -]New value: +[
      +  {
      +    "route": "22"
      +  }
      +]
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
      +  {
      +    "_apiKey": "your-cta-api-key",
      +    "route": "22"
      +  }
      +]
  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, and non-destructive behavior. The description adds useful behavioral context: live snapshot data, the exact fields returned, and the CTA bus tracker as the source. It does not mention data freshness limits or API-key handling, but the annotation coverage lowers that burden.

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?

Two tightly packed sentences plus a short example. The first sentence front-loads the action, scope, and return fields; the second gives a natural-language question and invocation. Every part earns its place with 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 simple read-oriented tool with a required route parameter and no output schema, the description is nearly complete: it names the returned fields and shows a working example. It does not explain empty-result behavior or data format details, but those are minor given the example and annotations.

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 explains route and _apiKey thoroughly. The description reinforces route value with the '22' example and the natural-language use case, but adds little semantic meaning beyond what the schema already provides.

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 states a specific action and resource: live CTA bus positions for a route, listing exact payload fields (lat/lon, heading, destination, delayed flag, distance). It also gives a concrete question it answers and an example call. It is clearly distinguishable from siblings like cta_bus_predictions by emphasizing 'currently running' positions.

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 gives a clear use case: 'where is the 22 Clark bus right now', which tells an agent when to invoke it. It implies a contrast with prediction-style tools via 'every vehicle currently running', but does not explicitly name alternatives or state when not to use it.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.