Find the highest-rated seats on a specific aircraft configuration. Filter by cabin class (economy, premium, business, first) and number of results. Use when someone asks "What's the best seat on the United 777?" or "Best economy seat on Delta A321neo?"
| Name | Required | Description | Default |
|---|---|---|---|
| cabin | No | Filter by cabin tier. Omit for all cabins. | |
| limit | No | Number of seats to return (default 3, max 10) | |
| config_id | Yes | Aircraft configuration ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must carry the burden. It only states 'Find the highest-rated seats' without explaining how ratings are determined, if any mutations occur, or any other behavioral traits like auth or rate limits.
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?
The description is two sentences plus example usage, front-loading the core purpose. Every sentence adds value without waste.
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 tool with 3 parameters and no output schema, the description covers the basic purpose and usage. However, it lacks detail on what 'highest-rated' means or the output format, leaving some gaps.
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 coverage is 100%, so baseline is 3. The description repeats the filter possibilities (cabin class, limit) from the schema but adds no new semantic meaning beyond that.
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 the tool finds highest-rated seats on a specific aircraft configuration, with a clear verb+resource. It also provides example queries that distinguish it from sibling tools like seat scoring or picking.
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 says 'Use when someone asks...' with concrete examples, providing clear usage context. However, it does not mention when not to use or alternatives 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.
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.
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.
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.
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.