Aichi future value simulator: Linear Chuo Shinkansen, Centrair 2nd runway, Toyota EV investment, Expo legacy impact on land prices. Markdown report. | 愛知県将来価値シミュレーター。リニア・セントレア・トヨタ・万博レガシーの地価影響をMarkdownレポートで出力。
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | 対象市区町村（例: 名古屋市中区, 豊田市, 常滑市） | |
| horizon | No | 試算期間 | 10y |
| scenarios | No | シナリオ（all で全シナリオを一括試算） | |
| 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 context beyond annotations by specifying the exact simulation inputs (the four named projects) and the output format (Markdown report), which is useful behavioral information. No contradiction with annotations is present.
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, with two sentences (one English, one Japanese translation). It front-loads the core purpose and enumerates the key drivers. The bilingual duplication is slightly wasteful but not excessive. It earns a 4 for efficiency, losing one point for the redundant translation that adds no new information for an English-speaking agent.
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 4 parameters (1 required) and no output schema, the description covers the essential context: what it simulates (four named projects), and what it returns (a Markdown report). The schema handles parameter definitions, and annotations cover safety. The description does not detail the report's structure, but given the tool's simplicity and lack of an output schema, this is acceptable. It is sufficiently complete for an agent to call it correctly.
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 75% (city, horizon, scenarios each have descriptions; includeMarkdown lacks one). The description does not add substantial parameter semantics beyond what the schema already provides; it merely restates the scenario names in prose (e.g., 'Linear Chuo Shinkansen' corresponds to the 'linear_chuo' enum). The includeMarkdown parameter is self-explanatory from its name, so the description need not elaborate. Since schema coverage is high, a baseline of 3 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 states a specific verb and resource: 'Aichi future value simulator' that models the impact of four named projects (Linear Chuo Shinkansen, Centrair 2nd runway, Toyota EV investment, Expo legacy) on land prices, producing a Markdown report. This is clear and specific. However, it does not explicitly differentiate from sibling tools like 'simulate_landscape_impact' or 'scenario_what_if', which could overlap in purpose.
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 its siblings. It does not mention alternatives, preconditions, or situations where another tool would be more appropriate. An agent would have to infer the use case solely from the project names, which is insufficient given the large set of analysis tools available.
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.