What-If scenario analysis: simulate impact of new stations, commercial facilities, population changes on land prices and investment scores. 10 prefectures. | シナリオWhat-If分析。新駅・大型商業施設・人口変動の地価影響を試算。全10都道府県。
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | 市区町村（例: '名古屋市中村区'） | |
| scale | No | 規模感。large=大型施設・急成長など | medium |
| horizon | No | 3y | |
| scenario | Yes | シナリオ種別 | |
| prefecture | No | 都道府県名（和名/英名/ISO 3166-2 コード対応） | 愛知県 |
| 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, and the description's 'simulate impact' aligns with a non-destructive read operation. The description adds useful context beyond annotations by specifying the scope (10 prefectures) and scenario types. No contradictions exist.
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 short sentences with a bilingual summary, front-loading the core purpose without any unnecessary words. It is concise and well-structured.
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 read-only simulation tool with a fairly rich schema, the description covers purpose and scope (10 prefectures, scenario types). It doesn't describe the output format, but that's acceptable given no output schema and the read-only nature mentioned in annotations. The agent has enough context to correctly select and invoke the tool.
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 67% of parameters with descriptions (city, scale, scenario, prefecture), and the description adds context by naming example scenarios that map to the scenario enum (e.g., new stations, commercial facilities). However, it does not explain parameters like scale, horizon, or includeMarkdown, so the added meaning is modest. Given the moderate schema coverage, a middle score is appropriate.
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 the impact of specific scenarios (new stations, commercial facilities, population changes) on land prices and investment scores, with a defined scope of 10 prefectures. It is specific and not a tautology, but it does not explicitly distinguish itself from sibling simulation tools like simulate_aichi_future or simulate_landscape_impact.
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 a clear context: use this tool when you need to simulate what-if impacts on land prices and investment scores. However, it does not mention exclusions or contrast with alternatives, so while the context is clear, there are no explicit routing instructions.
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.