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MBTA MCP Server

MBTA MCP Server

by cubismod
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

mbta_get_prediction_stats

Get prediction statistics and accuracy metrics for a specific MBTA station and route to assess service reliability.

Instructions

Get prediction statistics and accuracy metrics for a station and route.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "station_id": "<station_id>",
      "route_id": "<route_id>"
    },
    "name": "mbta_get_prediction_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
station_idYesStation ID to get stats for
route_idYesRoute ID to get stats for

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention data freshness, pagination, rate limits, or error handling. The description only states the basic operation, leaving the agent unaware of important behavioral traits like whether the stats are real-time or historical.

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?

The description is extremely concise at one sentence, with no wasted words. It front-loads the action ('Get') and resource. However, the lack of any supporting structure (e.g., examples, context) limits its utility, though the brevity is appropriate for a simple tool.

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

Completeness3/5

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

For a tool with 2 simple parameters and no output schema, the description is minimal but covers the basic operation. However, it omits information about the output format, which may be expected when no output schema is present. Additionally, absence of behavioral details reduces completeness.

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%, with each parameter having a basic description. The description adds no additional semantic value beyond what the schema provides. Per the rule, high coverage allows a baseline of 3, but the description does not clarify formats or constraints, such as possible ID values or data sources.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves prediction statistics and accuracy metrics for a given station and route. The verb 'Get' and the resource 'prediction statistics' are specific, and the mention of 'station and route' distinguishes it from sibling tools like mbta_get_predictions, which likely return raw predictions. However, it could be more explicit about what specific stats are included.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like mbta_get_predictions or mbta_get_track_prediction. There are no statements about prerequisites, limitations, or scenarios where this tool is preferred. The agent must infer usage solely from the name and parameters.

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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