Renovation yield analysis: calculate acquisition cost, renovation cost, expected rent, gross/net yield for Nagoya neighborhoods. Includes future plan upside. | リノベ利回り分析。名古屋市の町丁目×物件条件から取得価格・リノベ費用・利回りを算出。
| Name | Required | Description | Default |
|---|---|---|---|
| ward | Yes | 名古屋市の区名 (例: 中区, 中村区) | |
| chochou | Yes | 町丁目名 (例: 栄三丁目, 名駅一丁目) | |
| floorArea | Yes | 専有面積 (㎡) | |
| buildingAge | Yes | 築年数 | |
| propertyType | No | 物件種別 | mansion |
| acquisitionPrice | No | 取得予定価格 (円)。省略時は推定 |
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 description's main added value is the geographic restriction (Nagoya) and the 'future plan upside' feature. It doesn't disclose any additional behavioral traits such as estimation methodology, error handling, or whether results are approximate. With annotations covering safety, this is acceptable but not rich.
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 with the primary purpose front-loaded. It avoids redundancy and quickly conveys the tool's scope. Minor improvement could be adding explicit output format details, but as a description it is 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?
The tool has no output schema, so the description must explain what the agent can expect. It lists the computed outputs (acquisition cost, renovation cost, rent, yields) but does not specify the structure, units, or whether results are estimates. Given the tool's complexity and lack of output schema, more detail would be needed for full 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 100%, so all parameters are defined in the schema. The description doesn't add per-parameter details beyond the schema, but it does frame how the inputs combine (e.g., acquisitionPrice is optional and estimated when omitted). This is a marginal addition over the schema, hence the baseline 3.
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 calculates acquisition cost, renovation cost, expected rent, and gross/net yield for Nagoya neighborhoods, with a note on future plan upside. It differentiates from siblings like recommend_renovation_targets by focusing on yield analysis rather than recommendations, 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 does not provide any guidance on when to use this tool versus alternatives. It only states the general purpose and geographic scope, leaving the agent to infer whether this is the right choice based on the need for yield calculations. No mention of exclusions or conditions that would route the agent elsewhere.
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.