get_population_outlookPopulation outlook to 2050 (将来人口推計): projected population at 2030/2040/2050 with decline rate, based on NIPSSR data. | 2030/2040/2050年の人口推計と減少率を返す。
| Name | Required | Description | Default |
|---|---|---|---|
| area | No | Target city (e.g. '名古屋市中区') — omit for full prefecture | 対象市区町村 | |
| prefecture | No | 都道府県名（和名/英名/ISO 3166-2 コード対応） | 愛知県 |
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, destructiveHint=false, and openWorldHint=false, so the description does not need to state the read-only nature. It adds useful context about the data source (NIPSSR) and the output years, which goes beyond annotations but does not delve into deeper behavioral aspects like rate limits or response format. No contradiction with annotations.
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 short but somewhat redundant: the second English sentence ('projected population at 2030/2040/2050 with decline rate') essentially repeats the first, and the Japanese sentence is a direct translation. Not every sentence earns its place, though it is front-loaded with the core purpose.
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?
Given the low complexity (2 optional params, no output schema, no nested objects), the description covers the essential information: what data is returned, the time horizon, and the data source. It does not explain return format details, but for this simple lookup tool, the description is sufficiently complete.
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%, meaning both parameters (area and prefecture) are thoroughly described in the schema. The description adds no additional semantic meaning beyond the schema, so it meets the baseline of 3 but does not exceed it.
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 specific verb ('Population outlook'/'projected population') and resource (population projections to 2050 with decline rate) based on NIPSSR data. It is unambiguous about what the tool does, though it does not explicitly differentiate from sibling tools like forecast_land_price_trend or simulate_aichi_future, which limits it to a 4 rather than a 5.
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?
No explicit guidance on when to use this tool versus alternatives, but the description implies usage for population projections to 2050. It does not mention exclusions or alternatives, making the usage context implicit rather than clearly stated.
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.