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Bart

by io.github.pipeworx-io

Bart Departures

bart_departures
Read-onlyIdempotent

Real-time BART San Francisco Bay Area train departures for a station — next train departure times in minutes, per destination, with platform, direction, train length (cars), line color, delay, and bikes allowed. Station accepts a 4-letter BART code ("EMBR", "SFIA") or a station name ("Embarcadero", "Downtown Berkeley", "SFO"). Answers "when is the next BART train". Example: bart_departures({ station: "Embarcadero" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
_apiKeyNoOptional: your own BART API key (defaults to the BART public key)
stationYesBART station — 4-letter code like "EMBR", "MONT", "SFIA", or a name like "Embarcadero", "Powell", "Downtown Berkeley"

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: +[
+  {
+    "station": "EMBR"
+  },
+  {
+    "station": "Downtown Berkeley"
+  }
+]
  2. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds valuable behavioral details: real-time data, accept 4-letter codes or station names, and lists all response fields (platform, direction, train length, line color, delay, bikes allowed). No contradiction.

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 compact (3 sentences), front-loads the key purpose, and ends with a concrete example. No redundant words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with 2 parameters and no output schema, the description fully explains inputs and expected output fields. It is complete for an agent to understand and invoke correctly.

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 coverage is 100%, so baseline is 3. The description adds context that station can be code or name and provides an example, but does not significantly augment the schema's parameter descriptions.

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 uses a specific verb ('Real-time BART departures') and resource ('station'), lists output fields (next departure times in minutes, per destination, platform, direction, train length, line color, delay, bikes allowed). It distinguishes from sibling tools like bart_advisories and bart_trip_planner by focusing on immediate departures.

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 states the tool answers 'when is the next BART train' and gives an example query. It does not explicitly mention when not to use or compare to siblings, but the purpose is clear enough for an agent to select appropriately.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the three ask_pipeworx variants (stable, beta, grounded) overlap significantly in functionality, as do the Polymarket tools (bet_research, arbitrage, edges, etc.), which could cause an agent to misselect without careful reading of descriptions.

Naming Consistency3/5

Naming follows multiple styles: verb_noun (ask_pipeworx, compare_entities), domain first (polymarket_arbitrage, bart_departures), and single words (remember, forget). There is no consistent pattern, making the set feel disjointed.

Tool Count3/5

35 tools is on the heavy side for a single server. While each tool has a defined role, the count suggests potential for consolidation (e.g., merging ask_pipeworx variants or Polymarket tools). The server's broad scope partially justifies the count.

Completeness4/5

The tool surface covers a wide range of functionalities: structured querying, entity lookup, comparison, fact-checking, subscription management, memory, and domain-specific tools for BART and Polymarket. Minor gaps exist (e.g., no direct API for some data sources), but overall it feels comprehensive.

Resources