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Fair Fare: Check a New York taxi or ride-app driver's licence

fairfare_check_driver_licence
Read-onlyIdempotent

Fair Fare. Check a New York taxi or ride-app driver's licence. Whether a TLC licence number is active on New York City's daily open data for for-hire (Uber, Lyft, car service) and taxi drivers, its expiry date, and, if a name is given, whether it matches (the name on the licence is never returned). Other cities get the official way to check.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNoCity: nyc (live check) or another city id for guidance. Defaults to nyc.
nameNoOptional name the driver gave, to check it matches.
numberNoThe TLC licence number shown in the app or on the partition card.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, non-destructive, open-world. The description adds real value beyond them: the data source (NYC daily open data), what fields are returned (active status, expiry), and the non-obvious privacy detail that the licence holder's name is never returned even when matched.

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?

Front-loads the purpose and the returnable facts in dense but readable sentences. The trailing sentence 'Other cities get the official way to check' is a bit vague but still earns its place as routing guidance.

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?

With no output schema, the description carries the burden of describing returns and does so (active status, expiry, name-match result). A little more on the non-NYC guidance path would fully close the gap.

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 3 is the baseline, but the description adds semantics beyond the schema by clarifying that the name argument is verified for a match and that the matched name is never echoed back, and by confirming city defaults to nyc for a live check.

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?

States a specific verb (check) and resource (a New York taxi or ride-app driver's licence) with scope qualifiers (TLC number, NYC open data). It is clearly distinguishable from siblings like fairfare_check_fare or fairfare_check_fleet_file.

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?

Explains that the tool does a live NYC check when city=nyc and returns guidance for other cities, plus the conditional behavior when a name is supplied. It doesn't explicitly name a sibling alternative, but the usage context is clear.

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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