Skip to main content
Glama

MAQAMI Travel

get_data_hotel_ask

Read-onlyIdempotent

Overview

Beta Feature - Ask natural language questions about a specific hotel and get AI-powered answers based on the hotel's information.

When to Use

  • Hotel Q&A - Answer customer questions about hotels

  • Information lookup - Get specific details about amenities, services, or features

  • Conversational interfaces - Build chat interfaces for hotel information

  • Detailed inquiries - Ask about specific aspects like restaurants, parking, or amenities

What You Get

  • AI-generated answers - Relevant responses to your questions

  • Hotel-specific information - Answers based on the hotel's actual data

  • Natural language responses - Human-readable answers

Example Questions

  • "What amenities does this hotel have?"

  • "Is there parking available?"

  • "What does a meal at the restaurant look like?"

Key Features

  • Web search option - Enable allowWebSearch to get additional information from the web

  • Hotel context - Answers are specific to the hotel you're asking about

  • Natural language - Ask questions conversationally

Quick Start

Provide the hotelId and your question. Optionally enable allowWebSearch for web-enhanced answers.

Note: This is a beta feature and may be subject to changes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe question to ask about the hotel
hotelIdYesUnique ID of the hotel (liteAPI format)
allowWebSearchNoWhether to allow web search for additional information. Default is false.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, openWorld, non-destructive, so the safety profile is covered. The description adds genuinely new context (beta status subject to change, web-enhanced answers via allowWebSearch), but says nothing about answer reliability, latency, or failure modes for an AI-answer tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The markdown-heavy structure is far longer than a 3-parameter read tool warrants, with heavy redundancy ('natural language' is asserted in the overview, what-you-get, and key-features sections). The core instruction is buried in the final 'Quick Start' section.

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?

With no output schema, the description carries the return-value burden and does so adequately (AI-generated natural-language answers grounded in hotel data), plus example questions clarify expected input. Coverage is good for a simple read-only tool, only lacking answer-quality caveats.

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 description coverage is 100%, so all three parameters are already documented, making 3 the baseline. The description only restates hotelId, the question, and allowWebSearch, and inconsistently refers to the question as `question` while the schema names it `query`, adding mild ambiguity rather than meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The overview states a specific verb and resource: ask natural-language questions about a specific hotel and receive AI-generated answers. This is clearly distinguishable from data-listing siblings like get_data_hotel or get_data_hotels, though no sibling is named explicitly.

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

Usage Guidelines3/5

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

The 'When to Use' section lists four scenarios (Q&A, information lookup, chat interfaces, detailed inquiries), which implies usage context. However, these are largely restatements of the same idea and there is no guidance on when to prefer this tool over get_data_hotel or get_data_hotel_search, and no exclusions.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources