Jump to related tools in the same category or review the original source on GitHub.

Search & Research @qiao101660 Updated 2/25/2026

Aihotel OpenClaw Plugin & Skill | ClawHub

Looking to integrate Aihotel into your AI workflows? This free OpenClaw plugin from ClawHub helps you automate search & research tasks instantly, without having to write custom tools from scratch.

What this skill does

A Skill for searching hotels and querying prices via AIGoHotel MCP (searchHotels / getHotelDetail / getHotelSearchTags)

Install

npx clawhub@latest install aihotel

Full SKILL.md

Open original
Metadata table.
namedescription
aigohotel-mcpA Skill for searching hotels and querying prices via AIGoHotel MCP (searchHotels / getHotelDetail / getHotelSearchTags)

SKILL.md content below is scrollable.

AIGoHotel MCP Hotel Search Skill

This Skill describes how the model should use the aigohotel-mcp MCP server to search for hotels, view detailed prices for a specific hotel, and fetch available hotel tag metadata.

When to use this Skill

  • When the user wants to find hotels in a specific place (city, airport, attraction, station, detailed address, or a specific hotel), call searchHotels.
  • When the user has already chosen a specific hotel and wants to see detailed room types and prices (including cancellation policies, etc.), call getHotelDetail.
  • When you need to understand or explain available hotel tags (such as “Free WiFi”, “Family-friendly hotel”, “Airport hotel”), or need to map natural-language preferences into structured tags, call getHotelSearchTags.

Tool 1: searchHotels

Purpose: Search a list of hotels based on destination, dates, star rating, number of guests, budget, and tag filters.

Key input fields (fill them based on the user’s intent as much as possible):

  • Required

    • originQuery (string): The user’s original natural-language query.
    • place (string): The place name (city / airport / attraction / train station / subway station / hotel / district / detailed address, etc.).
    • placeType (string): The type corresponding to place. Supported values: 城市 (city), 机场 (airport), 景点 (attraction), 火车站 (train station), 地铁站 (subway station), 酒店 (hotel), 区/县 (district/county), 详细地址 (detailed address).
  • Dates and length of stay

    • checkInDate (string, YYYY-MM-DD): Parse from user utterances when possible; if omitted or earlier than today, the service automatically uses “tomorrow”.
    • stayNights (number): Set according to how many nights the user wants to stay; can be omitted if not specified (default is 1).
  • Guests and country

    • adultCount (number): Number of adults per room; use default 2 if not specified.
    • countryCode (string): Two-letter country code, e.g. CN for China, US for the United States.
  • Price and star rating

    • starRatings (number[]): Star rating range. For example, if the user says “4 stars and above”, you can use [4.0, 5.0].
    • hotelTags.maxPricePerNight (number): If the user says “no more than X per night”, set this field in hotelTags (currency in CNY).
  • Tags and brands (from user preferences or getHotelSearchTags)

    • hotelTags.preferredTags (string[]): Tags the user would like to have (e.g. “Free WiFi”, “Premium hotel”, “Family-friendly hotel”).
    • hotelTags.requiredTags (string[]): Tags that are must-have conditions.
    • hotelTags.excludedTags (string[]): Tags that the user explicitly does not want.
    • hotelTags.preferredBrands (string[]): Preferred brands, such as “Hilton”, “Marriott”, “Home Inn”, etc.
    • hotelTags.minRoomSize (number): Minimum room size in square meters, if mentioned by the user.
  • Others

    • distanceInMeter (number): When place is a POI, you can limit the straight-line distance; a typical value is 5000.
    • size (number): Number of hotels to return. Default is 5; generally should not exceed 10–20.
    • withHotelAmenities (boolean): Set to true when you need to compare hotels by amenities (such as pool, gym, etc.).
    • language (string): Locale, e.g. zh_CN for Chinese, en_US for English.
    • queryParsing (boolean): Usually keep the default true to leverage server-side intent parsing.

Output highlights (how to use the result):

  • Common top-level fields: message, hotelInformationList.
  • Each hotel in hotelInformationList contains:
    • hotelId, name, address, destinationId, latitude, longitude, distanceInMeters,
    • starRating, score, tags, hotelAmenities,
    • bookingUrl (you can provide this as a booking link to the user),
    • price (an object rather than a single number):
      • hasPrice: Whether price is available.
      • currency: Currency code (usually CNY).
      • lowestPrice: The lowest available price under the current conditions.
  • Some fields may be missing or null. Do not fabricate values that are not returned by the MCP tool.

Tool 2: getHotelDetail

Purpose: After the user has chosen a specific hotel, get detailed room types, total prices, and cancellation policies for a given stay period.

Typical call scenarios:

  • The user says things like “For this hotel, show me the room types and prices / whether breakfast is included / whether it’s refundable”.
  • Typically you first call searchHotels to find a suitable hotel and remember its hotelId, then call this tool.

Key input fields:

  • hotelId (number): Prefer using the hotelId returned from searchHotels.
  • name (string): Only use name-based fuzzy matching when you do not have a reliable hotelId.
  • checkInDate / checkOutDate (string, YYYY-MM-DD):
    • If empty, invalid, or earlier than today, the service will automatically adjust (e.g. use “tomorrow” and +1 day).
  • adultCount (number, default 2), childCount (number, default 0), childAgeDetails (number[]):
    • Set according to the actual number and ages of guests.
  • roomCount (number, default 1): Number of rooms.
  • countryCode (string, default CN): Country code.
  • currency (string, default CNY): Currency code.

Output highlights:

  • On success, the response usually contains:
    • success, errorMessage, hotelId, name, bookingUrl, checkIn, checkOut,
    • roomRatePlans: A list of room types and rate plans.
  • Common fields in roomRatePlans:
    • roomTypeId, roomName, roomNameCn, ratePlanId, ratePlanName,
    • currency, totalPrice (total price for this stay), inventoryCount, isOnRequest,
    • cancellationPolicies: Cancellation rules (time periods, amounts, descriptions, etc.).
  • When responding to the user:
    • Focus on summarizing key information: main room types, price range, whether free cancellation is available, whether breakfast is included, etc.
    • You may provide bookingUrl (e.g. “You can complete the booking via this link.”).
  • On failure, the tool may return an error message directly (e.g. “Failed to get prices, please try again later”). In that case, explain the failure in natural language and suggest adjusting dates or trying again later.

Tool 3: getHotelSearchTags

Purpose: Fetch metadata for tags that can be used in searchHotels.hotelTags. It is recommended to cache this on the client side and use it to map user intent to tags.

Output highlights:

  • tags: A list of tags, each including:
    • name: Tag name (for example “Free WiFi”).
    • category: Category (for example “Core Facilities”, “Hotel Type”, etc.).
    • description: Human-readable explanation.
  • usageGuide:
    • tagUsage: How to use tag names in hotelTags.preferredTags / requiredTags / excludedTags.
    • exampleRequest: Example request JSON.

Typical categories (non-exhaustive):

  • Brand & Rating
  • Selling Points
  • Core Facilities
  • Family & Kids
  • Service Details
  • Dining & Service
  • Transport & Payment
  • View & Room Type
  • Hotel Type
  • Price Related

When you need complex filtering with tags and are not sure about the exact tag names, you can first call getHotelSearchTags, then choose appropriate tags from tags.name to fill into hotelTags.


General dialogue and safety constraints

  • In multi-turn conversations, remember the user’s already-provided destination, dates, number of guests, star rating, and budget as much as possible, and only ask again when information is missing or ambiguous.
  • Never fabricate fields or values that are not returned by the MCP tools, especially for sensitive information such as prices and cancellation policies.
  • Make it clear that prices and availability are real-time results and may change over time.
  • Answer in the same language as the user (use Chinese for Chinese users, English for English users). Keep responses reasonably concise and highlight the most useful information (e.g. 3–5 recommended hotels or a few key room options), instead of dumping very long lists.

Local MCP configuration example (optional)

In development tools that support local MCP configuration (such as Cursor), you can add the following to mcp.json to mount AIGoHotel MCP as an available server (the name aigohotel-mcp-server is just an example and can be adjusted as needed):

{ "mcpServers": { "aigohotel-mcp-server": { "url": "https://mcp.aigohotel.com/mcp", "headers": { "Authorization": "Bearer <YOUR_AIGOHOTEL_MCP_TOKEN>", "Content-Type": "application/json" } } } }

Original Repository URL: https://github.com/openclaw/skills/blob/main/skills/qiao101660/aihotel
Latest commit: https://github.com/openclaw/skills/commit/786e193a5ebccef52b2fb263c95778761b50cd7a

Related skills

If this matches your use case, these are close alternatives in the same category.

1

Personal knowledge base powered by Ensue for capturing and retrieving understanding. Use when user wants to save knowledge, recall what they know, manage their toolbox, or build on past learnings. Triggers on "save this", "remember", "what do I know about", "add to toolbox", "my notes on", "store this concept".

academic-deep-research

Transparent, rigorous research with full methodology — not a black-box API wrapper. Conducts exhaustive investigation through mandated 2-cycle research per theme, APA 7th citations, evidence hierarchy, and 3 user checkpoints. Self-contained using native OpenClaw tools (web_search, web_fetch, sessions_spawn). Use for literature reviews, competitive intelligence, or any research requiring academic rigor and reproducibility.

academic-writer

Professional LaTeX writing assistant. Capabilities include: scanning existing LaTeX templates, reading reference materials (Word/Text), drafting content strictly following templates, and compiling PDFs. Triggers include: 'write thesis', 'draft section', 'compile pdf', 'check latex format'. Designed to work in tandem with 'academic-research-hub' for citation retrieval.

academic-writing

You are an academic writing expert specializing in scholarly papers, literature reviews, research methodology, and thesis writing. You must adhere to strict academic standards in all outputs.## Core Requirements1. **Output Format**: Use Markdown exclusively for all writing outputs and always wrap the main content of your response within <ama-doc></ama-doc> tags to clearly distinguish the core i...

academic-writing-refiner

Refine academic writing for computer science research papers targeting top-tier venues (NeurIPS, ICLR, ICML, AAAI, IJCAI, ACL, EMNLP, NAACL, CVPR, WWW, KDD, SIGIR, CIKM, and similar). Use this skill whenever a user asks to improve, polish, refine, edit, or proofread academic or research writing — including paper drafts, abstracts, introductions, related work sections, methodology descriptions, experiment write-ups, or conclusion sections. Also trigger when users paste LaTeX content and ask for writing help, mention "camera-ready", "rebuttal", "paper revision", or reference any academic venue or conference. This skill handles both full paper refinement and section-by-section editing.

aclawdemy

The academic research platform for AI agents. Submit papers, review research, build consensus, and push toward AGI — together.