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

Annotations already declare readOnlyHint and idempotentHint true. The description adds value by specifying return details: curated events with typical dates, attendance, URLs, notes, GPS coordinates. 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 only two sentences, no unnecessary words. Front-loaded with purpose and usage, then return details. Every sentence 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?

Given the tool has no required parameters and low complexity, the description is complete. It covers purpose, usage, returns, and exclusions. Output schema exists (though not shown), so return details are adequately 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% for all 3 parameters. Each parameter has detailed schema descriptions (month with examples, type with allowed values, prefecture with support). Description does not add additional semantics beyond schema, so 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 explicitly states 'recurring Japan events' and lists specific types (fireworks, matsuri, winter festivals), making the purpose clear. It also distinguishes from sibling tools by stating what not to use it for (bloom timing, one-off concerts, weather forecasts).

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 clear usage guidance: when to use (for recurring events to plan around) and what not to use (for bloom timing, etc.). It does not explicitly name sibling tools as alternatives but the exclusion criteria effectively define scope.

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 and idempotentHint. The description adds that it returns citation URL and text, and that it fetches live data for certain IDs, 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 sentences, front-loaded with the main purpose, no extraneous information. Every word contributes to understanding.

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 single-parameter tool with high schema coverage and output schema, the description is adequate. It explains the tool's role in a workflow (after search) and hints at live data behavior.

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% and the schema parameter description already includes examples. The tool description mentions the parameter implicitly but does not add significant new meaning 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 the tool's purpose: 'retrieve a full Japan in Seasons result with citation URL and text' after search. It distinguishes itself from siblings by specifying it is a follow-up to search, while siblings are specific data tools.

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 says 'Use this after search', providing clear context. Also mentions handling live sakura/autumn leaves IDs, implying usage for those cases. Does not explicitly state alternatives, but the sibling list provides 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 declare readOnly and idempotent, so the description adds value by detailing the returned data (peak windows, URLs, notes, GPS). 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 clear, front-loaded sentences with zero waste. Every sentence provides essential 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?

With 100% schema coverage, an output schema, and clear purpose/usage guidelines, the description is fully adequate for the tool's complexity.

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 the description does not need to add parameter details. It mentions output fields but not parameter specifics. 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 is for non-sakura flower trips (plum, wisteria, etc.) and returns curated spots with specific details. It explicitly distinguishes from sakura and koyo tools.

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 tells when to use (non-sakura flowers) and when not to (cherry blossom or autumn leaves), with specific alternatives (sakura or koyo 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 indicate readOnly and idempotent. The description adds that month filtering automatically narrows to in-season fruits, which is a behavioral detail beyond 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?

Three front-loaded sentences: first states use case, second describes behavior, third gives alternative. No irrelevant information; every sentence serves a 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 tool has 4 optional parameters, read-only behavior, and an output schema, the description covers essential context: what it returns, filtering behavior, and when to use alternatives. Complete for effective agent usage.

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% (baseline 3). The description adds value by explaining that month filtering narrows results automatically and mentions case-insensitive matching, enhancing understanding 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?

The description clearly states the tool provides fruit-picking farms, booking links, and map coordinates. It distinguishes from sibling fruit_seasons by specifying when to use that alternative.

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 explicitly tells when to use this tool (when user needs actual farms) and when not to (if only season info, call fruit_seasons first), providing clear alternative guidance.

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 read-only and idempotent behavior. Description adds context about returning calendar, peak months, regions, and notes for 14 fruits, which is valuable 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?

Two sentences front-load the use case and return information. Every sentence is necessary, no redundancy. 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?

Given the tool's simplicity (1 optional parameter, output schema present), the description fully covers what the tool does, what it returns, and how to proceed. No 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 the only parameter 'month' well-described in the input schema (range, behavior). Description does not add additional parameter semantics beyond what schema provides, so 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?

Description clearly states the tool's purpose: returning fruit season info for a given month or best months for specific fruits. It uses specific verbs ('returns') and resource ('fruit season calendar'), and distinguishes from sibling 'fruit_farms' which handles farm listings.

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 states when to use (user asks about fruit seasons) and when not to (use fruit_farms for listings). Provides a clear alternative and next step, making selection unambiguous.

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 tool has annotations readOnlyHint=true and idempotentHint=true, which already cover safety. The description adds minimal behavioral context ('routes to the right live dataset and returns a ready-to-use recommendation'), which is helpful but not substantial beyond 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 three sentences, front-loaded with purpose and usage, and every sentence adds value. No 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?

Given the tool has 5 optional parameters, an output schema, and numerous sibling tools, the description is complete. It tells what the tool does, when to use it, and when not to, which is sufficient for an agent to correctly select and invoke 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%, so the schema already documents all five parameters. The tool description does not add extra parameter-level detail beyond what is in 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 explicitly states the tool is for broad Japan seasonal travel questions, listing specific topics like cherry blossoms, autumn leaves, and festivals. It clearly differentiates from siblings by noting it is the best entry point for natural traveler prompts and contrasts with non-seasonal tools.

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 when-to-use ('when the user asks a broad Japan seasonal travel question') and when-not-to-use ('Do not use for hotels, flights, trains, visas, restaurants, or generic itinerary planning unrelated to seasonal timing'). This is exemplary guidance.

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 and idempotentHint, so the description's burden is lower. It adds context by detailing the returned data (forecast comment, map links, spot coordinates, bloom percentages). It does not mention rate limits or auth needs, but these are not critical given the read-only nature. No contradictions 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 concise (4 sentences) and front-loaded with usage conditions, followed by return values and a prohibition. Every sentence adds value with 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?

Given the tool's simplicity (2 optional params, output schema exists, no required fields), the description is complete: it covers when to use, what is returned, and what not to use. No gaps for an agent to make correct invocation decisions.

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 clear descriptions for both parameters. The tool description does not add additional meaning beyond what the schema provides (e.g., it mentions 'full list of Kawazu viewing spots' which aligns with include_spots but does not introduce new insights). 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 purpose: it returns Kawazu cherry blossom forecast data including JMC comment, map links, and spot details. It explicitly identifies the target user requests (January-February, Kawazu-zakura, early blossoms, Izu Peninsula) and distinguishes from standard sakura tools, making it unambiguous.

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 guidelines: when to use ('January-February cherry blossom requests', 'specifically asks about Kawazu-zakura, early blossoms, or the Izu Peninsula') and when not to use ('Do not use this for standard Somei-Yoshino sakura elsewhere'). This directly helps the agent select the correct tool from siblings like sakura_forecast.

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?

Beyond annotations (readOnlyHint, idempotentHint), description discloses that it checks overlap of trip dates with each city's koyo window based on forecast peak dates. 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 sentences, front-loaded with purpose, then usage conditions and exclusions. Every word adds value; no 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?

With full schema coverage, output schema present, and clear differentiation from siblings, the description is fully adequate for an agent to select and invoke the tool correctly.

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 already covers both parameters with format and constraints. Description reinforces their role as trip dates but adds no new semantic detail beyond what 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?

Clear verb 'returns' and resource 'cities' with specific conditions (autumn travel dates). Distinguishes from sibling by specifying 'maple or ginkgo viewing windows' and not for general climate or exact park recommendations.

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 states when to use (autumn travel dates, want best cities) and when not to (general climate, no dates). Provides alternative sibling tool 'koyo_spots' when prefecture is known.

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?

Beyond the readOnlyHint and idempotentHint annotations, the description adds valuable behavioral context: it returns city-level forecast dates, maps, and regional commentary from Japan Meteorological Corporation, and specifies the time period (October-December). 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?

Two sentences covering purpose, when to use, what returns, and exclusion criteria. Every word adds value with zero 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 annotations (readOnly, idempotent) and the presence of an output schema, the description fully informs the agent about the tool's scope, outputs, and limitations. No 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 covers 100% of parameters with clear descriptions. The tool description adds no new semantic detail for parameters beyond what's in the schema, 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 uses precise verbs ('forecast') and a specific resource ('autumn leaves peak'), and clearly distinguishes from sibling tools like koyo_spots and koyo_now by stating it provides forecasts, not current conditions or specific spots.

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 states when to use (peak timing, city comparisons, national overview) and when not to use (specific temples/gardens), with a direct alternative: 'call koyo_spots next for those.'

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 and idempotentHint, so the description does not need to repeat safety. It adds context about the data source (Japan Meteorological Corporation maple and ginkgo forecast data) and the nature of the answer (concise, current). 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 three sentences, front-loaded with the key directive, and every sentence adds value. 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?

The description covers purpose, usage, and exclusions. An output schema exists so return values are documented elsewhere. For a simple query tool with optional parameters, this is sufficient. It does not discuss pagination or edge cases, but that is 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% with clear descriptions for all parameters. The description adds no additional detail beyond the schema, so the 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 answers broad autumn leaves prompts with a concise current answer from live Japan Meteorological Corporation data. It includes example queries and explicitly excludes cherry blossoms and other topics, but does not explicitly distinguish from closely related koyo siblings like koyo_forecast or koyo_best_dates.

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 explicitly states when to use ('Use this first for broad autumn leaves prompts'), provides example queries, and specifies when not to use (cherry blossoms, fruit picking, hotels, etc.). This gives strong guidance for choosing this tool over alternatives.

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 and idempotentHint, and description adds behavioral context: returns specific fields (dates, leaf type, popularity, GPS), per-prefecture operation, and top-destinations when prefecture omitted. 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?

Three sentences: primary use, outputs, alternative/restrictions. Every sentence adds unique value. Front-loaded and 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?

Given one optional parameter, full schema coverage, and output schema exists, description provides complete context: use case, output details, parameter behavior, exclusions, and sibling references.

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% and describes the parameter well. Description adds context about omitting prefecture for top destinations, reinforcing schema. Single parameter is fully documented.

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 verb 'get exact spots' and resource 'autumn leaves viewing spots'. It distinguishes from siblings by specifying use case (user knows prefecture) and explicitly warns against using for cross-city date matching, directing to koyo_forecast or koyo_best_dates.

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 states when to use (user knows prefecture), when to omit prefecture (for top destinations guide), and when not to use (cross-city date matching). Provides alternative tools (koyo_forecast, koyo_best_dates).

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 and idempotentHint true, so safety is covered. The description adds that it uses observed or forecast full-bloom dates and returns overlapping cities, providing useful behavioral context without contradicting 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?

Three sentences, each purposeful: usage context, output description, exclusion and alternative. Front-loaded and efficient with 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?

With annotations covering safety, well-described schema parameters, and an output schema existing (as per context signals), the description is complete. It also addresses sibling differentiation, making it sufficient for an AI agent to select and invoke correctly.

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 descriptions including format and example. The tool description does not add new parameter meaning, but schema already explains them well. 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 returns cities with sakura viewing windows overlapping given travel dates. It specifies the verb 'returns' and the resource 'cities', and explicitly distinguishes from kawazu_forecast for early-bloom requests.

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 states when to use ('when the user provides travel dates and wants to know where sakura is likely to be best') and when not to use ('Do not use for January-February early-bloom Kawazu requests; use kawazu_forecast'), providing clear guidance and an alternative.

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?

Description adds behavioral context beyond annotations: specifies data source (Japan Meteorological Corporation), what data is returned (forecast dates, full-bloom, observed, historical averages, status), and scale (48 observation cities). Annotations already indicate read-only and idempotent, so bar is lower; description adds significant value.

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, front-loaded with purpose, no redundancy. Every sentence earns its place: first for when to use, second for what it returns and sibling guidance.

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?

Tool has one optional parameter, output schema present, annotations cover read-only/idempotent. Description fully captures purpose, usage boundaries, return content, and sibling relationship. No 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 coverage is 100%, so baseline is 3. Description does not add parameter-specific detail beyond what schema provides, but it does contextualize the parameter's role by describing the overall tool scope. No extra semantics needed.

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 verb (use this), resource (cherry blossom timing), and scope (cities across Japan). Explicitly distinguishes from siblings like sakura_spots and koyo_forecast.

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?

Provides explicit when-to-use criteria ('when the user asks about cherry blossom timing...') and when-not-to-use instructions ('Do not use for specific parks or temples; call sakura_spots'). Also suggests alternative sibling 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 and idempotentHint=true, so the safety profile is clear. The description adds that it returns 'concise current answer', 'specific viewing spot suggestions when available', and 'next-step guidance', which are useful behavioral details.

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?

Four sentences, front-loaded with purpose and examples, followed by clear exclusions. 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 simple scope (3 optional params, output schema exists but not shown), the description fully covers when to use, what it does, and what it 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 description coverage is 100%, with clear descriptions for city, start_date, and end_date. The description reinforces parameter usage through examples like 'How is Kyoto sakura looking?' for the city parameter.

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 it handles broad cherry blossom prompts, uses live Japan Meteorological Corporation data, and gives viewing spot suggestions. It distinguishes from siblings by explicitly excluding autumn leaves and other topics.

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 says 'Use this first for broad cherry blossom prompts' with example queries, and lists exclusions. However, it does not compare directly with other sakura siblings like sakura_forecast or sakura_spots.

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 and idempotentHint, but the description adds significant behavioral context: fallback logic for observations (JMC reporter within 48 hours vs. bloom-meter estimate with stale secondary), and inclusion of JMA reference station summary. This 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 compact, front-loaded with usage guidance, covers behavior, and ends with exclusions. Every sentence is necessary and well-structured.

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 (output schema present, single parameter, no nested objects), the description is complete: it covers purpose, usage, behavioral logic, parameter details, and references sibling tools. The output schema accounts for return values, so no further detail 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%, and the description adds value by specifying accepted formats (English name or numeric code) and the limitation of returning one prefecture at a time. This enhances the schema description.

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: retrieving exact cherry blossom viewing spots with current status and GPS coordinates for a known prefecture. It distinguishes from siblings by specifying it is not for nationwide timing comparisons, directing to sakura_forecast or sakura_best_dates.

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 states when to use ('when the user already knows the prefecture') and when not to ('Do not use this for nationwide timing comparisons or date matching; use sakura_forecast or sakura_best_dates first'). Provides clear guidance for selection.

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 and idempotentHint; description adds detail about live dataset retrieval and returning result IDs for fetch, 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?

Three concise sentences front-load the purpose, include usage guidance, and no redundant information. Every sentence serves a 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?

With one parameter, clear description, comprehensive annotations, and knowledge of output schema, the description fully enables correct tool selection and invocation.

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?

Only one parameter (query), and the description adds natural-language examples (e.g., 'cherry blossom forecast') that enhance the schema's already complete description.

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?

Clearly states the tool performs 'retrieval' over the 'Japan in Seasons' dataset, distinct from sibling tools. Lists specific topics it handles and excludes unrelated areas like hotels or flights.

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?

Provides explicit positive use cases (cherry blossoms, autumn leaves, etc.) and negative guidance (do not use for hotels, flights, etc.), leaving no ambiguity about when to choose this tool over 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 and idempotentHint true, so the description's added value is limited to return content details and city scope. No contradictions, but does not add significant 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?

Extremely concise: two sentences, zero waste. The first sentence clearly states the primary use case, the second provides exclusions and alternatives. Front-loaded with key guidance.

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 an output schema exists (not shown but indicated) and the tool has only one parameter, the description covers all necessary aspects: purpose, usage context, and constraints. No gaps for an AI agent to invoke correctly.

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%; the description does not add meaning beyond what the schema's property description already provides. The tool has only one parameter, and the description clarifies the city-specific scope, but this is implicit in 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 uses a specific verb ('returns') and specifies the resource ('next 3 days of Japan Meteorological Agency forecast text, temperatures, and 6-hour rain probabilities for one supported city'). It also distinguishes from sibling tools (sakura/koyo forecasts).

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 states when to use ('short-range weather could change recommendation') and when not to ('Do not use for seasonal bloom timing months in advance'), with clear alternatives (sakura or koyo forecast tools).

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