Roomza Hotel Intelligence

Ask about a hotel

ask_about_hotel

Read-onlyIdempotent

Ask a specific question about a hotel that standard data may not answer. Uses AI with web search to find the answer. Examples: 'Does this hotel have Eiffel Tower views?', 'Is there a rooftop bar?', 'How far is it from the airport?'. Try get_hotel first; use this only when that data doesn't answer the question.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`hotel_id`	Yes	Hotel UUID (the URL slug also works)
`question`	Yes	The specific question about the hotel

Output Schema

TableJSON Schema

Name	Required	Description	Default
`answer`	Yes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed1 schema field changedSep 2, 2026
- changedInput schema / properties / hotel_id / description
```
Previous value: -"Hotel UUID"New value: +"Hotel UUID (the URL slug also works)"
```

Changed1 schema field changedAug 17, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "answer": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "answer"
+  ],
+  "type": "object"
+}

First observedAug 17, 2026

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds meaningful behavioral context beyond annotations by disclosing that the tool uses AI with web search and that it answers non-standard questions. This is valuable since openWorldHint alone does not explain the mechanism or typical use case.

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?

The description is three sentences long, front-loads the core purpose, then provides concrete examples, and ends with precise routing guidance. Every sentence earns its place and there is no redundant or filler content.

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?

The description fully covers what the tool does, the type of question it accepts, and the relationship to get_hotel. With an output schema present and annotations covering safety and open-world behavior, no critical information is missing for an agent to select and invoke this tool correctly.

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 description coverage is 100%, so both parameters are already documented. The description adds value by providing concrete example questions that clarify what kind of 'question' is expected, helping the agent form valid inputs. While not strictly necessary, this enriches the parameter semantics beyond the schema's generic descriptions.

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 states a specific verb ('ask'), resource ('hotel'), and the distinctive scope: questions that standard data may not answer, answered via AI with web search. It is clearly differentiated from the get_hotel sibling, which is named explicitly as the alternative for standard data.

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?

The description gives explicit routing: 'Try get_hotel first; use this only when that data doesn't answer the question.' This clearly defines when to use this tool versus its closest sibling and leaves no ambiguity about the intended workflow.

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

All Roomza Hotel Intelligence tools Inspect

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TDQS

A4.4/5.0

Disambiguation5/5

Each tool has a clear, distinct purpose: searching, retrieving details, pricing, rooms, reviews, nearby places, user-specific actions (preferences, trips), booking, and presentation. The overlap between get_hotel and ask_about_hotel is intentional and clearly differentiated (ask is for supplementary questions), and get_hotel_rooms vs show_rooms are also distinct (list vs present).

Naming Consistency5/5

All tools follow a consistent verb_noun pattern: get_hotel, get_hotel_pricing, get_hotel_rooms, get_my_preferences, get_my_trips, get_nearby, get_recent_reviews, search_hotels, secure_room, show_rooms, add_special_request, ask_about_hotel. The only minor variation is 'ask_about_hotel' but it's still verb-based and follows the same structure.

Tool Count5/5

Twelve tools is well within the optimal range for a domain of this scope. The toolset covers the core lifecycle (search, view, price, book, add request, view trips) plus supporting functions (reviews, nearby, user preferences, presentation). No redundant or superfluous tools exist.

Completeness4/5

The toolset covers the primary hotel intelligence and booking workflow thoroughly: search, detail, pricing, rooms, booking, special requests, and user data. Slight gap: no explicit cancellation or booking modification tool, but given the server's focus on intelligence and presentation, this is a minor omission that agents can work around.

Resources

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