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seat-intelligence

by com.rowhint

rowhint_get_config_overview

Get a summary of an aircraft configuration including total seats, cabin layout, best and worst seats per cabin, and facility locations (galleys, lavatories). Use when someone asks "Tell me about the American Airlines 787-9" or wants a general overview before choosing a seat. Also use to discover valid config_ids for other tools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
airlineNoAirline slug (e.g., american-airlines). Use with aircraft.
aircraftNoAircraft slug (e.g., boeing-787-9). Use with airline.
config_idNoDirect configuration ID (e.g., AA-789). Takes precedence if both provided.

TDQS

A3.8/5.0
Behavior2/5

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

No annotations provided, so the description carries full burden. It describes the tool as a read operation ('Get a summary'), but doesn't discuss safety, idempotency, authorization requirements, or any side effects. More detail on behavioral traits would be beneficial.

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?

Two well-structured sentences: the first defines purpose and output, the second gives usage context and secondary function. No unnecessary words, front-loaded with critical info.

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?

Given no output schema and three optional parameters, the description lists the key outputs (seats, cabin layout, facilities) and usage scenarios. It could mention response format or edge cases, but overall it's sufficient for the tool's straightforward nature.

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% (all three parameters have descriptions). The description adds one extra piece: that config_id takes precedence if both airline/aircraft and config_id are provided. This is useful but doesn't go beyond the schema substantively.

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 clearly states the tool retrieves a summary of an aircraft configuration, listing specific details (total seats, cabin layout, best/worst seats, facility locations). It distinguishes from sibling tools by emphasizing it's for a general overview before seat selection, while siblings focus on comparisons, best seats, scores, etc.

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?

Explicitly says to use when someone asks about a specific aircraft or wants a general overview before seat choice, and also to discover valid config_ids. It lacks explicit 'when not to use' scenarios, but the guidance is clear and helpful.

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

Tools are mostly distinct, with clear purposes like comparing seats, getting best seats, overview, score, windowless, etc. Slight overlap between get_best_seats and pick_seat, as both recommend seats but for different contexts (config vs. flight). Otherwise, each tool has a unique role.

Naming Consistency4/5

All tools use the 'rowhint_' prefix and snake_case verb_noun pattern. Most are consistent (e.g., get_*, compare_*, pick_*), but 'help_me_pick' and 'seat_roast' break the pattern slightly with imperative phrases or noun-verb order. Overall, naming is clear and predictable.

Tool Count5/5

With 8 tools, the server covers a reasonable scope for seat intelligence: overview, recommendations, specific seat info, comparison, and a conversational picker. The count is well-balanced, not too few or too many for the domain.

Completeness4/5

Core use cases are covered: best seats, specific seat scoring, comparison, windowless seats, and flight-based picks. Minor gaps include lack of a tool to list all seats on a config with full details, but the conversational picker and overview tool mitigate this. Overall, agents can accomplish key tasks without dead ends.

Resources