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

Annotations declare read-only, idempotent, and non-destructive behavior. The description adds context about using an external API (Google Maps) and computing an overall score, which is beyond the annotations. 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 concise sentences in English and Japanese, front-loaded with purpose. Every sentence adds value without 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 explains the core functionality, mentions the external API, and indicates an overall score. With an output schema present, return values need not be detailed. Minor gap: no mention of limitations or refresh behavior, but openWorldHint covers variability.

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 already documents all parameters. The description adds no additional parameter meaning. 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's function: analyzing transit commute accessibility to regional station hubs using the Google Maps Distance Matrix API. It specifies outputs (travel times, routes, overall score) and differentiates from sibling tools focused on other property assessments.

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 commute accessibility analysis but provides no guidance on when not to use this tool or alternatives. It mentions the API reference but lacks explicit when-to-use or when-not-to-use context.

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 safe, idempotent, non-destructive behavior. The description adds value by disclosing the reliance on specific external APIs and the fallback to simulated results when keys are unconfigured, providing important 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 concise with two sentences, front-loading the core purpose. The second sentence adds important fallback context. No unnecessary 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 tool's complexity (7 parameters, no required, output schema present), the description adequately covers purpose, API dependencies, and fallback behavior. It provides sufficient context for an AI agent to understand the tool's capabilities.

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 clear descriptions. The description does not add significant new meaning beyond noting the use of Street View (which relates to heading/pitch) and Gemini. 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 tool's purpose: an AI visual exterior audit using Google Maps Street View and Gemini Vision. It identifies the resource (property exterior) and the action (assess), and distinguishes itself from siblings by mentioning the use of specific APIs and fallback 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 when an exterior visual audit is needed and mentions fallback behavior when API keys are missing, but it does not explicitly state when to choose this tool over siblings like quick_visual_summary or assess_family_friendly_score.

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, idempotentHint=true, destructiveHint=false. The description adds context about the three axes and prefecture scope but does not disclose additional behavioral traits like response format or performance limits. 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 two short sentences (English and Japanese) that efficiently convey the core purpose. However, the bilingual repetition is slightly redundant; a single clear statement might be more concise.

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 6 parameters, nested objects, and an output schema, the description is too minimal. It does not explain how to use the various location parameters (area vs prefecture vs neighborhood) or what the output contains. The Japanese translation adds no new context.

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 67%, covering most parameters. The tool description does not add meaning beyond what the schema already provides; for example, it does not explain how area, prefecture, or neighborhood interact. Baseline 3 is appropriate given high schema coverage.

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 assesses family-friendliness across three axes (education, safety, healthcare) for housing suitability, limited to 10 prefectures. This distinguishes it from sibling tools like assess_exterior_visuals or assess_property_risk, which target different 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 implies the tool is for evaluating family-friendliness, but does not explicitly state when to use it versus alternatives like analyze_commute_accessibility or predict_corporate_demand. No when-not-to-use guidance is 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?

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds that the tool provides integrated scoring and covers 10 prefectures, but does not elaborate on other behavioral traits (e.g., rate limits, response format, or error conditions).

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 (two sentences) and front-loaded with key information. The Japanese line is redundant but not detrimental. Every sentence contributes value, though merging English and Japanese could improve efficiency.

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 parameters, nested objects, output schema), the description adequately covers the tool's purpose and key features but omits details on parameter usage (e.g., latlng vs. address, how prefecture/neighborhood interact) and does not explain the integrated scoring mechanism. The existence of an output schema partially compensates.

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 60%, with descriptions for address, prefecture, and neighborhood. The description adds context that riskTypes include flood, landslide, and earthquake and mentions coverage across 10 prefectures, but does not clarify the latlng object or required address parameter beyond what the schema states.

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 assesses property disaster risk for flood, landslide, and earthquake, with integrated scoring across 10 prefectures. This verb+resource+scope combination distinctly separates it from sibling tools like 'assess_family_friendly_score' or 'forecast_land_price_trend'.

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, nor does it mention any prerequisites or exclusions. It merely describes functionality without contextual selection advice.

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, idempotentHint, destructiveHint=false. Description adds that the tool returns formatted Markdown content with specific sections, providing useful behavioral 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?

Two concise English sentences followed by Japanese translation. Front-loaded with verb-object structure, no unnecessary 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 single parameter, existing output schema, and annotations, the description fully covers the tool's function and output without gaps.

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%, with clear explanation and example for the 'id' parameter. The description does not add additional parameter semantics beyond what the schema provides.

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 fetches a document by ID from search results and returns specific content (area analysis, forecasts, summaries) in Markdown. This distinguishes it from sibling tools like 'search' which returns IDs.

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?

Explicitly indicates usage context ('from search results'), implying it should be used after a search. Lacks explicit when-not-to-use or alternative tool references.

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 the tool as read-only, idempotent, and non-destructive. The description adds behavioral specifics: the forecasting methods (linear regression, moving average), return values (CAGR, confidence interval, investment signal), and a coverage constraint (10 prefectures). This provides useful context beyond the annotations without contradiction.

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 in English with a Japanese translation, front-loaded with the core purpose. Every element (method, outputs, coverage) is necessary and informative, with no redundant or vague phrasing.

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 has 7 parameters and an output schema, the description covers the core functionality and outputs well. However, it does not explain the '10 prefectures' limitation or mention the 'output_mode' and 'includeMarkdown' parameters, which are configurable. The output schema likely addresses return values, so the description is mostly complete but could be more explicit about constraints.

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 the schema already documents most parameters. The description mentions methods (linear regression, moving average) corresponding to the 'method' parameter and alludes to outputs, but does not detail parameters like 'city', 'horizon', or 'landUse'. Thus, the description adds limited parameter-specific 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?

The description clearly states the tool's purpose: 'Forecast land price trends using linear regression and moving average.' It specifies the outputs (CAGR, confidence interval, investment signal) and mentions coverage (10 prefectures). The verb 'forecast' and resource 'land price trends' are specific, and the description distinguishes this tool from siblings like 'assess_property_risk' or 'portfolio_optimizer' by its focus on price trend prediction.

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 the tool is for land price forecasting but does not provide explicit guidance on when to use it versus alternatives (e.g., 'scenario_what_if' or 'predict_corporate_demand'). No when-not-to-use instructions or exclusions are given, though the context of price trend analysis is clear.

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, consistent with the description's output focus. The description adds that it returns expected return, risk, Sharpe ratio, but does not elaborate on behavioral traits beyond what annotations provide. 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?

Two concise sentences in both English and Japanese, front-loaded with the core purpose and key outputs. No unnecessary 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 tool has a complex input schema (5 params) and an output schema exists, the description sufficiently covers the tool's purpose and return values. It lacks mention of optimization goals or risk tolerance options, but these are in the schema. Near complete for selection purposes.

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 80%, and the input schema already describes parameters with decent detail (e.g., enum options, descriptions). The description only adds that the tool handles up to 5 areas, which is already in schema via maxItems. 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 optimizes a real estate investment portfolio across up to 5 areas and returns specific metrics (expected return, risk score, Sharpe ratio). This distinguishes it from sibling tools like scenario_what_if or assess_property_risk, which have different purposes.

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 implicitly states the tool is for optimizing portfolios, but provides no explicit guidance on when to use it versus alternatives (e.g., scenario_what_if for scenario analysis). No when-not-to-use or prerequisites are 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 readOnly, idempotent, and non-destructive. The description adds context about sectors and geographic scope but does not elaborate on rate limits, authentication, or other behavioral traits. 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 extremely concise: two short sentences covering purpose and scope. Front-loaded with the verb. No superfluous 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?

The description covers the main purpose and sectors, but lacks specificity about which prefectures are included and does not mention the output format even though an output schema exists. Leaves some ambiguity about geographic scope.

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?

Input schema has 80% description coverage, so baseline is 3. The tool description does not add any additional meaning or usage context for the parameters. Parameters like 'neighborhood' have schema descriptions but the tool description does not explain how they affect the prediction.

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 predicts corporate demand scores for manufacturing, office, and retail across 10 prefectures. However, the input schema includes property types like logistics and commercial not mentioned, causing slight inconsistency. It distinguishes from sibling tools like assess_family_friendly_score by focusing on corporate demand.

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 sibling tools list is provided but no explicit context about when to choose predict_corporate_demand over tools like scenario_what_if or portfolio_optimizer.

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, idempotentHint=true, destructiveHint=false, signaling a safe, idempotent read operation. The description adds behavioral details such as 'compact markdown fallback' and optimization for ChatGPT, 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 sentences (one English, one Japanese) and front-loads the purpose. The bilingual repetition adds length but serves the target audience. It is efficient for its content.

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 main output components and usage context. With an output schema available, the description does not need to detail return format. It provides a solid understanding of what the tool does and when to use it.

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%, with each parameter having a clear description. The tool description adds high-level output context (map, charts, actions) but does not provide additional semantics for individual parameters beyond the schema. 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 renders a 'real estate visual summary with map, charts, recommended next actions, and compact markdown fallback.' It also explicitly says to use this tool when the user asks to show, visualize, compare, or continue in ChatGPT, distinguishing it from sibling tools like scenario_what_if or assess_family_friendly_score.

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 explicit usage guidance: 'Always use this when the user asks to show, visualize, compare, or continue in ChatGPT.' This clearly defines the context for invocation, though it does not list specific exclusions or when not to use the tool.

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, idempotentHint=true, destructiveHint=false. The description only says 'simulate impact', adding no further behavioral context about rate limits, authentication needs, or any side effects 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?

Two sentences, bilingual, front-loaded with key action and scope. Every word adds value; no redundancy or fluff.

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 essential function and scope, and an output schema exists to explain return values. However, it lacks details on limitations (which 10 prefectures, how results are tabulated) and could be more complete for a complex simulation 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?

With 67% schema coverage, the schema already documents most parameters adequately. The description adds no extra meaning beyond summarizing the tool's overall function. A score of 3 is appropriate as baseline.

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 impact of various scenarios on land prices and investment scores, with a specific scope of 10 prefectures. It effectively distinguishes from sibling tools like forecast_land_price_trend or simulate_landscape_impact by focusing on what-if analysis.

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 the tool is for exploratory scenario analysis but does not provide explicit guidance on when to use it versus alternatives. No when-not-to-use conditions or comparisons to siblings are given.

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, idempotentHint=true, destructiveHint=false. The description adds 'ChatGPT-compatible' and mentions returning a list, which aligns but does not significantly enhance transparency 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 extremely concise with two short sentences in English and equivalent in Japanese. Every word serves a purpose 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?

For a simple search tool with one parameter and an output schema existing (though not detailed), the description adequately explains the purpose and return type. No additional context is needed.

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% for the single 'query' parameter, which has a description in the schema. The tool description does not add extra parameter information, so baseline 3 applies.

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 action: 'Search the real estate data catalog for areas, tools, and data sources.' It includes a resource ('real estate data catalog') and distinguishes from sibling tools which are more specific analytical tasks.

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 when-to-use or when-not-to-use guidance is provided. The usage is implied by the sibling tools (all specific analyses), but no exclusions or alternatives are 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?

Adds behavioral context beyond annotations: specifies data sources and analysis scope (surrounding buildings). No contradiction with readOnlyHint/idempotentHint/destructiveHint.

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, bilingual. Slightly repetitive but no fluff. 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?

Adequate for core purpose, but could mention need for internet access to fetch PLATEAU data. Output schema exists, so no need to describe returns.

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%, above 80% baseline. Description does not add extra parameter-specific details beyond 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 simulates sunlight/shadow using PLATEAU 3D buildings and SunCalc. Distinguishes from siblings like 'scenario_what_if' or 'assess_exterior_visuals'.

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?

Implied usage for sunlight/shadow analysis but no explicit when-to-use or when-not-to-use guidance. No alternatives mentioned.

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