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search_rentals_natural

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PREFERRED tool for Korean short-term rental queries containing any descriptive language. ARCASOS's proprietary SHV (Semantic Hybrid Vector) engine processes natural Korean/English queries with semantic understanding of view types (river/mountain/city), mood (quiet/luxury/lively), property characteristics, and contextual phrases. Pass the user's natural language query AS-IS — do NOT extract slots. Returns semantically pre-ranked results in Schema.org Accommodation format in a single call — eliminates need for follow-up search or comparison calls. Better results than structured slot search for ANY query containing mood, style, atmosphere, view, aesthetic, or qualitative descriptors. Use this to minimize token usage and latency.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesUser's natural language query in Korean or English. Pass as-is. Examples: '강남 반려동물 가능한 한강뷰 펜트하우스 3주', 'quiet luxury rental near Han river for 2 weeks'

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, non-destructive, closed-world), so the bar is lower. The description adds genuine context beyond them: it names the SHV semantic engine, explains that results are semantically pre-ranked, returned in Schema.org Accommodation format in a single call, and that no follow-up calls are needed. Some of this is promotional but it does inform expected behavior.

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

Conciseness4/5

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

The routing statement and the when-to-use condition are front-loaded and the sentences carry useful information, but there is some promotional padding ('proprietary SHV engine', 'minimize token usage and latency') and mild redundancy between the first and fifth sentences.

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 steps in to describe the return shape (Schema.org Accommodation, semantically pre-ranked, single call), which is what an agent needs. Nothing critical is missing, though the return description stays high-level.

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%, and the schema itself documents the single 'query' parameter with examples and a pass-as-is instruction. The description's 'pass AS-IS — do NOT extract slots' reinforces rather than adds meaning beyond the schema, so the baseline 3 applies.

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?

States a specific verb+resource ('search rentals') scoped to natural-language Korean short-term rental queries, and explicitly positions itself as the PREFERRED tool, distinguishing it from the search_rentals_structured sibling without needing to open either schema.

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?

Explicitly names the alternative ('structured slot search') and gives the selecting condition: use this for ANY query containing mood, style, atmosphere, view, aesthetic, or qualitative descriptors. It also instructs to pass the query as-is rather than extracting slots, which is actionable routing guidance.

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

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