Search the real estate data catalog for areas, tools, and data sources. ChatGPT-compatible. | 不動産データカタログを検索し、関連するエリア・ツール・データソースの候補一覧を返す。
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | 検索クエリ（自然文OK。例: "名古屋 リニア", "東京 投資", "リスク 地震"） |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds that it returns a candidate list and is 'ChatGPT-compatible,' implying natural language acceptance. It does not disclose result limits, pagination, or other behaviors, but given the annotation coverage, this is acceptable. No contradiction with annotations.
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 compact—two short sentences, with the English and Japanese versions. It front-loads the core purpose. The phrase 'ChatGPT-compatible' is somewhat superfluous but not harmful. No unnecessary fluff.
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 single-parameter search tool with read-only annotations, the description is minimally complete: it states what it searches and that it returns a candidate list. The absence of an output schema is mitigated by the clear intention, but the agent lacks detail on result structure or how to interpret candidates. Given the tool's simplicity, this is acceptable but not thorough.
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?
The schema covers the only parameter 'query' with 100% coverage, including a detailed description with examples. The tool description adds no parameter-specific information beyond what the schema already provides. Baseline of 3 is appropriate when the schema fully documents the parameter.
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 identifies the verb (Search) and resource (real estate data catalog) and lists the covered entity types (areas, tools, data sources). It implicitly distinguishes itself from search_area_candidates by covering more than areas, though it does not explicitly name the sibling. The purpose is unambiguous.
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?
No explicit guidance is given on when to use this tool versus alternatives like search_area_candidates or the analysis tools. The phrase 'ChatGPT-compatible' is vague and does not clarify contextual fit. The description neither states exclusions nor recommends conditions for use, leaving the agent to infer.
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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Many tools have overlapping purposes (e.g., analyze_renovation_yield vs recommend_renovation_targets, multiple scoring functions). While descriptions provide some differentiation, an agent could easily confuse tools like assess_property_risk, assess_family_friendly_score, and composite_value_score, all of which aggregate multiple axes into a single score.
Most tools follow a verb_noun pattern (analyze_, assess_, get_, simulate_, etc.), but a few deviate with noun phrases (composite_value_score, portfolio_optimizer, scenario_what_if) or adjective-led names (quick_visual_summary). The pattern is largely consistent with minor exceptions, making it predictable overall.
With 33 tools, the surface is quite heavy and exceeds the 25-tool threshold. While the server covers a broad domain (real estate analysis, simulation, contract review, reporting), many tools could be consolidated (e.g., multiple scoring functions). The count feels overwhelming for an agent to manage efficiently, though the scope is comprehensive.
The tool set covers the primary workflows of real estate intel: search/discovery, data retrieval, scoring, simulation, reporting, and contract support. Minor gaps exist (e.g., no direct property transaction listing lookup or lease-specific analysis), but these are not core to the server's stated purpose. The lifecycle of analysis is well-supported.