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Cta

by io.github.pipeworx-io

Cta Bus Predictions

cta_bus_predictions
Read-onlyIdempotent

Chicago CTA bus tracker arrival predictions at a bus stop — route, destination, predicted minutes until arrival ("DUE" = arriving now), delay flag, and vehicle id. Pass stop_id (the 4-5 digit stop number posted on CTA bus-stop signs), optionally with route. If you only know the stop by name, pass route + find_stop (a street/intersection fragment like "clark & madison") and the stop is looked up for you. Example: cta_bus_predictions({ route: "22", find_stop: "addison" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
maxNoMax predictions to return, 1-20 (default 10)
routeNoOptional bus route number to filter, e.g. "22", "66", "X49". Required when using find_stop.
_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)
stop_idNoCTA bus stop id (stpid), the number on the bus-stop sign, e.g. "1926". Comma-separable up to 10.
directionNoOptional direction to narrow find_stop, e.g. "Northbound", "south"
find_stopNoStop-name fragment to look up when stop_id is unknown, e.g. "clark & madison", "michigan & randolph". Requires route.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-cta-api-key",
+    "route": "22",
+    "stop_id": "1926"
+  },
+  {
+    "_apiKey": "your-cta-api-key",
+    "direction": "Northbound",
+    "find_stop": "clark & madison",
+    "route": "66"
+  }
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations declare readOnlyHint, idempotentHint, destructiveHint=false, covering safety. Description adds behavioral details: returns predictions with 'DUE' meaning now, and lookup behavior for find_stop. No contradictions.

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?

Two sentences plus example, front-loading purpose and key patterns. Information-dense but not wasteful. Could be slightly more concise, but effective.

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?

Covers main usage patterns (stop_id and find_stop), key output fields, and lookup behavior. Optional params like max, direction, _apiKey are documented in schema; description omits them, but schema fills gaps. Good enough for a read-only tool with full schema descriptions.

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?

Schema coverage is 100%, so baseline 3. Description adds meaning beyond schema: explains stop_id comes from sign, find_stop is a fragment requiring route, and provides a concrete example. Adds value.

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?

Description clearly states it provides bus arrival predictions at a stop, listing output fields (route, destination, minutes, delay, vehicle id). Sibling tools like cta_bus_positions and cta_train_arrivals are differentiated by context and verb.

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?

Clearly distinguishes two usage patterns: use stop_id when known, or route+find_stop when only stop name is known. Provides example. Does not explicitly exclude alternative tools, but context implies this is for predictions vs. positions.

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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TDQS

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all accept natural-language factual questions, and ask_pipeworx_beta is explicitly described as currently identical to ask_pipeworx. The entity tools (entity_profile, compare_entities, recent_changes, resolve_entity) and the many Polymarket tools also blur together, making misselection likely.

Naming Consistency3/5

Most names are snake_case and readable, with recognizable prefixes like cta_, polymarket_, and ask_pipeworx_. However, conventions are mixed: bare verbs (remember, forget, subscribe), noun-style phrases (entity_profile, bet_research), and variants like ai_visibility_check vs scan_competitor_ai_presence prevent a single predictable pattern.

Tool Count2/5

35 tools is above the comfortable range, and the count is especially mismatched for a server named 'Cta': only 4 tools actually concern Chicago transit, while the other 31 form a general-purpose research, prediction-market, and memory suite. The set feels like multiple unrelated servers merged together.

Completeness2/5

As a CTA server, the surface is notably incomplete: it has bus/train positions and predictions but lacks alerts, service disruptions, route listings, and station/stop metadata. The broader Pipeworx tools are extensive but appear bolted on, so the overall set has no coherent domain against which completeness can be judged.

Resources