Search municipality name candidates by partial text. Supports hiragana. | 市区町村名の候補検索。部分文字列から有効な市区町村候補を返す。ひらがな対応。
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | 最大候補数（1-20、デフォルト20） | |
| query | No | 市区町村名の一部（例: 名古屋, なごやしなか, 新宿） | |
| prefecture | No | 都道府県名（例: 愛知県, 東京都） |
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 convey readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds useful behavioral context beyond annotations: it specifies support for hiragana input, which is not obvious from the schema and indicates a specific matching behavior. It does not detail rate limits, error handling, or return format, but given annotations cover the read-only nature, the description contributes meaningful extra behavioral details.
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 succinct: two sentences separated by a delimiter, with the primary English statement followed by a Japanese translation. The core purpose is front-loaded, and every clause serves a purpose—stating function, scope, and an important feature. There is no fluff or redundancy, making it highly efficient.
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 search tool with three optional parameters fully documented in the schema, the description is largely complete. It specifies the input behavior and the type of output ('candidates'). While it does not explain the return structure or pagination, the absence of an output schema and the simplicity of the task lower the need for such detail. The description covers the essential context for correct usage.
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?
Schema description coverage is 100%, so each parameter (limit, query, prefecture) is already documented. The description adds a minor hint about query semantics by mentioning 'partial text' and hiragana support, but it does not add deeper meaning beyond the schema. The limit and prefecture parameters are not elaborated in the description, but since the schema covers them, the description meets the expected baseline.
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 states the tool's function: 'Search municipality name candidates by partial text.' The verb is specific ('Search'), the resource is specific ('municipality name candidates'), and the mode ('by partial text') is explicit. The additional note 'Supports hiragana' further clarifies the search behavior. This distinguishes it from sibling tools like the generic 'search' and others focused on analysis rather than lookup.
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?
The description implies when to use the tool (when you need to find municipality names from partial text) but does not explicitly state when not to use it or mention alternatives. For instance, the sibling 'search' might be a more general alternative, but the description does not address differentiation. There is no guidance on prerequisites or fallback options, so it offers only an implied usage context.
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.