Live subway alerts, delays, and planned-work advisories from the official MTA GTFS-RT feed for NYC lines. Other cities can be requested via the free /agent/request-data endpoint.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | ||
| line | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full weight. It states the data source (official MTA GTFS-RT feed) and liveness ('Live'), implying a read-only, non-destructive operation. It does not mention rate limits or authentication, but given the context, the transparency is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two compact sentences. The first sentence packs the core purpose and source. The second sentence adds useful cross-reference. No wasted words, though the second sentence could be clearer about the alternative endpoint's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 2-parameter tool with no output schema, the description covers the main functionality. However, it lacks details on parameter constraints (e.g., valid city values) and the exact output format, leaving some gaps for a fully complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add meaning to parameters. It only implies 'city' is for 'nyc' and 'line' is a subway line (example 'L'), but does not enumerate valid values or provide additional semantic guidance beyond the schema's basic types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides 'Live subway alerts, delays, and planned-work advisories from the official MTA GTFS-RT feed for NYC lines.' This is a specific verb-resource pair (get live transit status) and distinguishes it from siblings like getRailStatus by focusing on NYC subway.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly notes NYC as the primary city and mentions that other cities can be requested via a different endpoint, giving clear when-to-use context. It doesn't explicitly state when not to use, but the context is sufficient for selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools have overlapping functionality, e.g., auditNetworkHost combines DNS, SSL, and header checks that have dedicated tools (auditDnsSecurity, checkSslExpiry, auditSecurityHeaders). Multiple weather and blockchain tools also overlap in scope, making it difficult for an agent to choose the right tool.
Most tools follow a consistent verb_noun pattern (e.g., getAirQuality, checkDnsPropagation), but a few deviate (agentPreflight, capabilitiesDiff) and some use compound names (depositCoordinationBounty). Overall, the pattern is clear but not perfectly uniform.
With 56 tools, the server is far too large for a coherent MCP surface. The number suggests a collection of many unrelated APIs rather than a focused tool set. A typical well-scoped server has 3-15 tools.
While the tool set covers many domains, each domain has shallow coverage. For example, blockchain tools miss basic transaction sending and contract deployment; weather tools lack forecasts. The 'requestMissingData' endpoint acknowledges gaps, but the current surface is severely incomplete for a general-purpose API.