Drill-down local analysis at block/neighborhood level including foot traffic, commercial, education. Markdown output. | 街区ドリルダウン分析。町丁目レベルの詳細分析。Markdown出力。
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | 市区町村（例: '名古屋市中村区'） | |
| focus | No | all | |
| prefecture | No | 都道府県名（和名/英名/ISO 3166-2 コード対応） | 愛知県 |
| exportFormat | No | 出力フォーマット。xlsx を指定すると xlsxBase64 フィールドに Base64 エンコード済み Excel を返す | json |
| neighborhood | No | 町丁目（例: '名駅南1丁目'）。v2.4 では町丁目レベル実データに対応（対応都道府県のみ） |
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 declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds that output is in Markdown, but this is partly redundant with the exportFormat parameter. It does not disclose limitations such as supported prefectures (hinted in the neighborhood param description) or data freshness.
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 two concise sentences, front-loading the purpose and scope. The bilingual repetition in the second sentence adds no new information but is not disruptive. Overall it is efficient and avoids unnecessary verbosity.
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?
With no output schema, the description should clarify what the tool returns, but it only states 'Markdown output' without explaining the structure or that exportFormat can also produce JSON/XLSX. It also omits prerequisites like supported prefectures, though this is partially covered in the 'neighborhood' parameter description. The description is adequate for basic orientation but lacks completeness.
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 80%, with 'focus' lacking any description. The tool description adds no parameter-specific semantics; it merely repeats the output format. While the schema covers most parameters, the gap for 'focus' is not compensated, so the description provides little added value.
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 a specific action (drill-down analysis) and resource (block/neighborhood level), listing the data domains covered (foot traffic, commercial, education). This distinguishes it from area-level tools like compare_prefectures or generate_area_report, making the purpose 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?
The description implies granular, local analysis via the phrase 'at block/neighborhood level', but does not explicitly instruct when to use this tool versus siblings or when not to. No alternatives are mentioned, leaving the agent to infer suitability based on scope.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.