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Glama

Ride options and prices

uber_ride_options
Read-onlyIdempotent

Compare live Uber ride options for a trip, including price, tier, seats, ETA, and matched pickup/dropoff before requesting a ride.

Instructions

Every ride option Uber offers for this trip right now: name, tier, seats, live price (fixed upfront price, or a range for a metered taxi), pickup ETA and arrival time, with the pickup and drop-off as Uber matched them (check them; free-text matches list alternatives). Read-only; requesting is uber_request_ride.

Input Schema

NameRequiredDescriptionDefault
pickupYesSaved place label, place id, 'lat,lng' or address
dropoffYesSaved place label, place id, 'lat,lng' or address
paymentNoPayment method id or name (default: the app's choice)

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.15

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive/open-world, so the bar is lower, and the description still adds real behavior: prices are live, fixed upfront or a metered range, and the returned pickup/drop-off are Uber's matched locations that may differ from free-text input. It stops short of noting rate limits, caching/staleness windows, or why matches diverge, which keeps it under a 5.

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, front-loaded with the payload contents and back-loaded with the safety/caveat note. It is dense — the parenthetical price explanation packs several clauses — but each detail is useful rather than filler.

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?

With an output schema present, the description needn't enumerate return values, yet it does so briefly and also covers the one behavioral caveat (location matching) that an agent would otherwise get wrong. Nothing needed to call it correctly is missing.

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% with each parameter documented (accepted formats, default payment), so the baseline is 3. The description reinforces that pickup/dropoff are subject to Uber's matching and that free text can resolve to alternatives, but adds no syntax or format detail beyond the schema and says nothing about the optional payment parameter.

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+resource ('every ride option Uber offers for this trip right now') and enumerates what is returned: name, tier, seats, price, ETA, arrival time. It also explicitly distinguishes itself from the write path by naming uber_request_ride, so an agent can separate it from siblings without opening a schema.

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

Usage Guidelines5/5

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

Explicitly says read-only and points to uber_request_ride as the alternative for actually booking. It also gives a concrete pre-call instruction ('check them; free-text matches list alternatives'), which is exactly the when/how guidance an agent needs.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.