Sunlight/shadow simulation using PLATEAU 3D buildings + SunCalc. | 日照・影シミュレーション。PLATEAU 3D建物データ+SunCalcで周辺建物の影響を分析。
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | 対象地点の緯度 | |
| lng | Yes | 対象地点の経度 | |
| radiusM | No | 建物検索半径（メートル） | |
| dateTime | No | シミュレーション日時（ISO 8601形式、省略時は現在時刻） | |
| prefecture | No | 都道府県名（和名/英名/ISO 3166-2 コード対応） | 愛知県 |
| timePreset | No | 時刻プリセット（morning=8:00, noon=12:00, evening=17:00） | |
| includeMarkdown | No |
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, so the safety profile is covered. The description adds the data sources (PLATEAU, SunCalc) and that it analyzes surrounding buildings, but doesn't disclose return format, performance characteristics, or regional limitations. With annotations covering safety, a 3 is appropriate.
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 concise and front-loaded with the main purpose in English, followed by a Japanese duplicate. The duplication adds length but serves bilingual users; it's efficient overall and lacks filler.
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 7 parameters and no output schema, the description should explain what the tool returns (e.g., whether it outputs a report, visual, or data) and any limitations (e.g., Japan-specific, prefecture default). It does not mention includeMarkdown or output format, nor when to prefer it over sibling tools. This leaves critical gaps for correct invocation.
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 86%, so the schema documents most parameters adequately. The description adds no parameter-specific semantics (e.g., meaning of radiusM, dateTime formatting), but the high coverage means the schema carries the burden. Baseline 3 is justified.
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 simulates sunlight/shadow impact using PLATEAU 3D buildings and SunCalc, with a specific verb ('simulate') and resource (landscape impact). It distinguishes itself from obvious siblings like simulate_aichi_future and simulate_leveraged_cashflow by focusing on geography-based light analysis, though it doesn't explicitly name alternatives.
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 provides no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. It simply states what it does, leaving the agent to infer usage context on its own.
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.