Japan Real Estate Intel

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 mention of 'calculate' adds no behavioral insight beyond confirming it's a read operation. The Japanese text provides localization context but no additional behavioral disclosure.

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 English sentences and two Japanese sentences. It front-loads key information (yield analysis, Nagoya) without unnecessary details. Slightly longer output description could improve completeness, but it remains 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?

While the description covers inputs and purpose, it lacks output schema details. The tool's return format is not described, which could confuse agents needing to interpret results. For a tool with 6 parameters and no output schema, this is a gap.

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 coverage is 100%, so all parameters have descriptions. The description does not add meaning to individual parameters beyond what the schema provides; it only summarizes the tool's outputs (yield, costs). Baseline score 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 is specific: calculates acquisition cost, renovation cost, expected rent, gross/net yield for Nagoya neighborhoods, including future plan upside. It clearly distinguishes from sibling tools like assess_property_risk or recommend_renovation_targets.

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 explicitly state when to use this tool versus alternatives, nor does it provide context for exclusion. It implies usage for yield analysis in Nagoya but lacks guidance on comparing to siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 statement about analyzing and returning risk score aligns with no mutation. The description adds minimal behavioral context beyond the annotations, such as the output being risk score and deal-breakers, but does not disclose data sources, limitations, or other traits.

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 sentences (English and Japanese), highly concise, and front-loaded with the key action and output. Every sentence adds value with no redundant or extraneous information.

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 description covers the output (risk score, deal-breakers) and example clauses, but does not explain the role of the location parameters (ward, chochou) in the analysis. Given the tool has no output schema, the description could be more complete about how the inputs relate to the output.

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 description adds meaning beyond the input schema by listing example clause types (financing contingency, inspection, future value terms), which clarifies what proposedTerms should contain. The schema only says 'JSON format' with no examples. However, the roles of ward and chochou are not explained beyond their schema descriptions.

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 verb (analyze/assess), resource (contract risk), and specific clause types (financing contingency, inspection, future value terms). It also provides a Japanese version, reinforcing the purpose. This distinguishes it from sibling tools like assess_property_risk.

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 guidance on when to use this tool or when not to. It does not mention alternatives among the many sibling tools. The description only states what it does, not the context or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations (readOnlyHint=true, destructiveHint=false) already indicate it's a safe read operation. The description adds behavioral context: it fuses axes, computes a score, and produces multiple output types. No contradictions. It could mention that the AI narrative requires an API key (parameter description does).

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 sentences, front-loading the core purpose and output features. The dual-language (English/Japanese) is a slight redundancy but not verbose. No wasted words.

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 complexity (5 axes, multiple outputs) and that an output schema exists, the description sufficiently covers what the tool computes and returns. It lacks discussion of horizon defaults or weight customization hints, but these are in the schema.

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%; all parameters are well-described in the schema. The description's mention of '5 axes' reinforces the weights parameter but adds minimal extra meaning. Baseline 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 fuses five specified axes into a single 0-100 score and lists outputs (radar, tier, peer comparison, AI narrative). This verb+resource+output set distinguishes it from sibling tools that focus on single aspects or different analyses.

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 holistic value assessment but gives no explicit guidance on when to use this tool versus alternatives like assess_family_friendly_score or forecast_land_price_trend. No 'when not to use' or context triggers are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds that it performs cross-referencing of data but does not disclose behavioral details such as data freshness, limits, or the need for MLIT_API_KEY (though mentioned in param). With annotations present, description adds moderate context but not enough for higher score.

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 extremely concise: one sentence in English and one in Japanese, with no filler. The key points (function, data sources, use cases) are front-loaded. Every word earns its place, making it highly efficient for an agent to parse.

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 that an output schema exists (not shown but noted) and all 5 parameters are documented with descriptions, the description covers the core scanning functionality. It lacks some nuance like what happens if no signals are found or how 'live' data behaves, but overall it's sufficiently complete for a scan tool with robust annotations and schema.

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?

All five parameters have descriptions in the input schema (100% coverage), so the schema already provides full meaning. The tool description does not add any new semantic information beyond what is in the param descriptions. Baseline 3 is appropriate as description offers no extra value.

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's function as a price triangulation scanner that cross-checks three property valuation metrics to detect discount buys, inheritance-tax edges, and overheated markets. It uses specific verbs like 'cross-checks' and 'detect', and the bilingual text reinforces the purpose. This distinguishes it from siblings that focus on individual property analysis or market snapshots.

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 when to use the tool (to find arbitrage opportunities between different valuation methods) and provides clear use cases. However, it does not explicitly state when not to use it or mention alternative tools for different scenarios (e.g., individual property analysis). Slight lack of exclusion guidance prevents a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, and destructiveHint. The description adds behavioral detail: methodology (linear regression, moving average), outputs (CAGR, confidence interval, signal), and geographic limitation (10 prefectures). No contradictions.

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, using two short English sentences (plus Japanese translation). It front-loads the purpose and key outputs, with no redundant information. Could be more structured, but overall 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?

With no output schema, the description mentions key outputs but lacks detail on return structure, error handling, or how parameters affect results. Partial compensation for missing output schema, but not fully 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 86%, so most parameter semantics are provided by the schema. The description adds no additional parameter-level detail beyond the overall tool purpose. 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 the tool forecasts land price trends using linear regression and moving average, and specifies outputs (CAGR, confidence interval, investment signal). It distinguishes from siblings by focusing on land price with specific methodology, and mentions geographic scope ('10 prefectures').

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 context for forecasting land price trends, but does not explicitly state when to use this tool versus alternatives (e.g., get_future_timeline, scenario_what_if). Usage is implied but lacks clear guidance or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it's safe and non-destructive. The description adds the geographic scope (Nagoya wards/neighborhoods) and output metrics, which is useful but not extensive behavioral detail.

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?

Two short sentences (English and Japanese) convey the core purpose immediately. No wasted words; each part is necessary.

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 no output schema, the description hints at return values (metrics list). It specifies geographic scope and output modes. Fairly complete for a straightforward read-only tool. Could mention if data is current year or default behavior, but acceptable.

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 coverage is 100%, but the description adds context by listing specific metrics ('land price, population, households, ongoing plans') not in the schema. It also explains output_mode options ('compact=TL;DR + key numbers only, detailed=full Markdown report'). This adds meaningful value beyond the schema.

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?

Description clearly states the tool returns current metrics (land price, population, households, ongoing plans) for Nagoya wards/neighborhoods. The verb 'get' and resource 'profile' are specific, and it distinguishes from sibling tools like get_population_outlook or get_real_estate_macro_snapshot.

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. The description only states what it does, not when to prefer it or exclude it. Usage is implied by the tool's name and purpose.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds that it returns a yearly timeline, which is consistent. No behavioral contradictions, but no further details on data source, update frequency, or pagination.

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?

Two sentences, no filler. Front-loaded with English then Japanese mirror. Every word adds value.

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 tool with 2 parameters and no output schema, the description covers purpose, scope, and output format (yearly timeline). Could be improved by specifying exact output structure (e.g., list of events with years).

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%. Description adds that chochou is optional and omitting returns ward-level data, but this is already implied by the schema's default empty string. Minimal added value.

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?

Description clearly states the tool returns a timeline of future redevelopment, infrastructure, and population projections for Nagoya wards/neighborhoods from 2025-2050, distinguishing it from sibling tools like get_population_outlook.

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?

Usage context is implied (future planning), but no explicit guidance on when to use versus alternatives. Sibling tools are listed but not distinguished from each other.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 clear. The description adds value by specifying exact output (population at 2030/2040/2050 and decline rate) and the data source (NIPSSR). No contradictions, and no missing behavioral details beyond what annotations provide.

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 well-structured sentences, one in English and one in Japanese, with no wasted words. It is front-loaded with the core purpose and key details.

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 simple read-only tool with no output schema, the description adequately explains the return values (population at three years, decline rate) and the data source. It lacks details on decline rate format or historical inclusion, but these are minor omissions given the tool's simplicity.

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 the schema fully documents both parameters. The description does not add parameter-specific semantics beyond what the schema provides, but it does clarify the tool's overall scope (e.g., based on NIPSSR data). Baseline 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 specifies the verb ('projected population') and the resource ('NIPSSR data'), and distinguishes from siblings by naming exact future years and the decline rate. No other sibling tool covers this exact output.

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 context (time points, data source) but no explicit guidance on when to use this vs. siblings like simulate_aichi_future or compare_prefectures. The usage is implied by the name and output, but alternatives are not mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral context by specifying the geographic scope (municipality) and the comparison to national average, which informs the agent of the tool's analytical nature.

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 contains two languages (English and Japanese) which adds redundancy. While front-loaded with the English line, the repetition makes it slightly less concise than necessary.

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?

No output schema is provided, but the description lists the expected return fields (total vacant, for-rent, for-sale, other, compared to national average). This is sufficient for an agent to understand the output structure, though more detail on data source could improve 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 the baseline is 3. The description does not add extra meaning beyond the schema's parameter descriptions, which already explain 'area' and 'prefecture' adequately.

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 'get' and resource 'vacancy rate statistics', and enumerates breakdowns (total, for-rent, for-sale, other) and comparison to national average. It is distinct from sibling tools like 'analyze_renovation_yield' or 'compare_prefectures'.

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, nor any mention of when not to use it. The description provides no context about prerequisites or preferred scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value by listing the specific data returned (zone type, ratios, height limits), which is a behavioral trait beyond the annotations. It does not disclose any potential constraints or side effects, but for a read-only lookup, this is sufficient.

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 extremely concise: two sentences (English and Japanese) that clearly convey the tool's function. There is no wasted text, and the important information is front-loaded in English.

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 simple lookup tool with full schema coverage and annotations, the description is complete. It explains what the tool returns (zone type, ratios, height limits) and is sufficient for an agent to understand its capabilities without an output schema. The context of sibling tools shows it is unique.

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 coverage is 100%, with each parameter having a bilingual description. The tool description does not add additional semantic meaning beyond the schema. Per guidelines, baseline is 3 when schema coverage is high, and the description does not enhance parameter understanding.

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's purpose: 'Look up zoning for an area' and lists specific outputs (zone type, coverage ratio, floor area ratio, height limits). It is a specific verb+resource combination and is distinct from sibling tools, which are primarily analytical or focused on other real estate aspects.

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 explicit guidance on when to use this tool versus alternatives. It does not mention when to avoid it or suggest alternative tools for other scenarios. The agent must infer usage from the purpose alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds that it returns specific metrics (return, risk, Sharpe ratio) but does not disclose additional behavioral traits such as authentication needs, rate limits, or effects on system state. With annotations covering safety, a score of 3 is appropriate for the minimal added context.

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 extremely concise: two sentences (English and Japanese), both front-loaded with the tool's purpose. Every word is necessary, with no filler or redundancy. It efficiently communicates the core functionality.

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 tool's complexity (5 parameters, nested objects, no output schema), the description is somewhat minimal. It lists the output metrics but does not explain how they are interpreted or what the output format looks like. The input is clear due to good schema coverage, but the output lacks detail. An adequate score considering the trade-offs.

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 coverage is high (80%), with most parameters having descriptive names and descriptions. The description adds a small amount of context ('across up to 5 areas') that aligns with the targets parameter's maxItems constraint, but does not elaborate on the meaning of 'optimizeFor', 'riskTolerance', or other parameters beyond what the schema provides. Baseline 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 the tool's action ('optimize'), resource ('real estate investment portfolio'), scope ('across up to 5 areas'), and outputs ('expected return, risk score, Sharpe ratio'). It effectively distinguishes itself from sibling tools by being a portfolio optimization tool, while siblings like 'scenario_what_if' focus on scenario simulation.

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 alternatives. It does not mention exclusions, prerequisites, or recommend when to choose portfolio_optimizer over other simulation or analysis tools. The default behavior is implied but not explicitly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds meaningful behavioral context by specifying the scope (scanning all 16 Nagoya wards) and the ranking functionality, which goes beyond the 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 two sentences (English and Japanese), highly efficient, and front-loaded with the core purpose. Every word earns its place with no redundancy.

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 description lacks output details such as the format of the ranking (e.g., what fields are returned). Without an output schema, the description should compensate by hinting at the return structure. It is adequate but incomplete for a ranking 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?

Schema coverage is 75% (3 of 4 parameters have documented descriptions). The description does not mention parameter details, but the schema already provides adequate information. No additional meaning is added, so baseline 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 scans all 16 Nagoya wards to rank neighborhoods by yield, providing a specific verb and resource. This purpose distinguishes it from siblings like 'analyze_renovation_yield' which likely focuses on individual properties.

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 this tool is for broad geographic comparison, but it does not explicitly state when to use it versus alternatives or provide exclusions. Usage context is implied but not explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that it returns scores and decisions but does not provide additional behavioral context beyond what annotations provide.

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 covering purpose and outputs. It is front-loaded but slightly dense; however, it remains 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 description covers key evaluation axes and outputs. However, with 25 parameters and complexity, it lacks prerequisites or data source context. Output schema exists, so return values are covered.

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 baseline is 3. The description does not add new parameter-level meaning beyond what the schema already describes.

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 evaluates asking price against various benchmarks and returns scores, decision, etc. However, it does not explicitly distinguish this tool from siblings like assess_contract_risk or assess_property_risk, which overlap in functionality.

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 guidance on when to use this tool versus alternatives. The description only lists what it does, without specifying when it is appropriate or when to use other tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint=true and destructiveHint=false. The description adds that it outputs a Markdown report, but does not disclose behavior like error handling, speed, or response format details beyond the report. Minimal extra context beyond 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 two sentences (English and Japanese), both front-loaded with key information: purpose, scenarios, output format. No unnecessary words; every sentence earns its place.

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 simulation tool with 4 parameters and no output schema, the description adequately conveys the tool's purpose and output (Markdown report). It could be improved by hinting at the return structure (e.g., JSON containing Markdown string), but is sufficient given the context and sibling tools.

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 coverage is 75% (3 of 4 parameters have descriptions). The description adds overarching context that the tool simulates impacts and outputs a report, which indirectly clarifies 'includeMarkdown'. However, it doesn't add significant meaning beyond the schema descriptions. Baseline 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 the tool simulates future land price impacts from specific Aichi projects (Linear Chuo Shinkansen, Centrair expansion, Toyota EV, Expo legacy) and outputs a Markdown report. This distinguishes it from siblings like 'simulate_landscape_impact' or 'simulate_leveraged_cashflow'.

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 use for Aichi future value simulation with named scenarios, but provides no explicit guidance on when to use this tool versus alternatives like 'scenario_what_if' or 'forecast_land_price_trend'. No exclusions or prerequisites mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnlyHint=true and destructiveHint=false, and the description adds that it uses PLATEAU 3D buildings and SunCalc, providing context about data sources and analysis scope. No contradictions.

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 sentences (English and Japanese) that front-load the core purpose without extraneous detail. Every word is essential.

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 7 parameters and no output schema. The description explains the simulation's data sources and purpose but does not specify the output format or what the user receives (e.g., markdown report). Given the complexity, some output detail is missing.

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 coverage is 86%, and all parameters have descriptions in the schema. The tool description does not add any parameter-specific meaning beyond what the schema already provides, so baseline 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 sunlight/shadow using PLATEAU 3D buildings and SunCalc, with a specific verb ('simulate') and resource ('landscape impact'). It is distinct from sibling tools, none of which focus on sunlight simulation.

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 sunlight/shadow analysis but does not explicitly state when to use or when not to use this tool versus alternatives. No exclusions or context for decision-making are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true and destructiveHint=false, making safety clear. The description adds value by listing specific outputs (NOI, DSCR, IRR, etc.) and outlining the overall calculation scope, complementing the 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?

Two concise sentences (English and Japanese) front-load the core purpose and outputs. Every sentence is informative with no redundancy.

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 tool's complexity (17 parameters, nested objects, output schema), the description adequately summarizes inputs and outputs. The presence of an output schema reduces the need to detail return values, but a bit more context on when to choose 'compact' vs 'detailed' mode would help.

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 coverage is 100% with detailed parameter descriptions. The description provides a high-level summary of input categories but does not add significant meaning beyond the schema. Baseline score 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 is a 'Leveraged 10-year real estate pro-forma' with specific inputs and outputs, distinguishing it from sibling tools like 'simulate_aichi_future'. The title and bilingual description reinforce the 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 leveraged cashflow analysis but does not explicitly state when to use versus alternatives like un-levered or other simulation tools. Sibling names suggest differentiation, but no direct guidance is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.