Future timeline: upcoming redevelopment, infrastructure, and population projections for Nagoya wards/neighborhoods (2025-2050). | 未来タイムライン。名古屋市の区・町丁目に影響する将来計画を年次タイムラインで返す。
| Name | Required | Description | Default |
|---|---|---|---|
| ward | Yes | 名古屋市の区名 (例: 中区) | |
| chochou | No | 町丁目名 (省略時は区全体) |
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 does not need to repeat safety. It adds context that the output is an 'annual timeline' (年次タイムライン) and covers future plans, but does not detail pagination, error behavior, or data structure. Given the annotation coverage, this is adequate 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 short sentences (English and Japanese) with zero filler. Key information—subject, scope, time range, and return format—is front-loaded. Every word earns its place, making it highly 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?
For a tool with no output schema, the description states it returns an 'annual timeline' of future plans, which partially explains the return. However, it lacks detail on the structure (e.g., whether results are grouped by year, include event types, or provide coordinates). Given the complexity of future-plan timelines, an agent might need more specifics, though the safety annotations reduce risk.
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% for both 'ward' and 'chochou', so the schema already documents parameter meanings. The description adds contextual value by linking parameters to 'Nagoya wards/neighborhoods' and specifying the 2025-2050 time frame, but it does not add syntax or format details beyond what the schema provides. 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 clearly states it returns 'upcoming redevelopment, infrastructure, and population projections' for Nagoya wards/neighborhoods over 2025-2050, which is specific and informative. It does not explicitly name alternatives among siblings like get_population_outlook or forecast_land_price_trend, but the combination of scope and time range distinguishes it well enough for an agent to understand its 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 implies usage for future-plan-related queries ('upcoming redevelopment, infrastructure, and population projections') and mentions the time range, but it does not explicitly state when to prefer this tool over similar siblings or provide exclusion criteria. It relies on the agent inferring applicability from the content.
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.