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seat-intelligence

by com.rowhint

rowhint_seat_roast

Roasts a specific airline seat. Returns a one-paragraph, voice-y takedown of the seat based on its quality score, proximity issues, recline/legroom data, and any notes. Use when someone asks 'how bad is seat 31B on the United 787-9?' — they want the unvarnished truth, not a neutral lookup.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seatNoSeat identifier (e.g., 31B). If omitted, the tool elicits it.
config_idNoAircraft configuration ID. If omitted, the tool elicits it from the user.

TDQS

A4/5.0
Behavior3/5

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

Describes input and output (quality score, proximity, etc.) but does not state whether the tool is read-only, requires permissions, or has any side effects. Given no annotations, the description carries the burden and is adequate but not exhaustive.

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 sentences that efficiently convey purpose, output, and usage context. No wasted words; front-loaded with key information.

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?

Covers what the tool returns and when to use it. With optional parameters and no output schema, the description is sufficient. Could be improved by noting it's a safe read operation.

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 descriptions for both parameters. The description adds no new parameter details beyond the schema, so baseline of 3 is appropriate.

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 'Roasts a specific airline seat' and specifies it returns a 'voice-y takedown'. It contrasts with neutral lookups, distinguishing it from siblings like 'rowhint_get_seat_score'.

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?

Provides a concrete example ('how bad is seat 31B on the United 787-9') and implies when to use it (unvarnished truth). Does not explicitly list when not to use or name alternatives, but context is clear.

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

All seat-intelligence toolsInspect
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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