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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions what the tool does (analyze statistics), it doesn't describe important behavioral aspects like whether this is a read-only operation, what format the output takes, whether it requires authentication, or any rate limits. The description is insufficient for a tool with no annotation coverage.

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 very concise - a single sentence in Chinese that gets straight to the point. There's no wasted language, though the '等' (etc.) creates some ambiguity. It's appropriately sized for what it communicates.

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 annotations and no output schema, the description is incomplete for a statistical analysis tool. It doesn't explain what the output looks like, what statistical methods are used, or how the analysis is performed. With 2 parameters and no structured output information, the description should provide more context about the tool's behavior and results.

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 schema description coverage is 100%, so the schema already fully documents both parameters (lottery_type with enum values and periods with range/default). The description adds no additional parameter information beyond what's in the schema, so it meets the baseline expectation but doesn't provide 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 states the tool analyzes lottery number statistics including hot and cold numbers, which provides a general purpose. However, it's somewhat vague ('等' meaning 'etc.' adds ambiguity) and doesn't clearly distinguish this from sibling tools like 'analyze_seq_numbers' or 'get_historical_data' that might also involve lottery number 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 provides no guidance on when to use this tool versus alternatives. With multiple sibling tools related to lottery analysis (e.g., 'analyze_seq_numbers', 'backtest_lottery', 'predict_lottery'), there's no indication of what makes this tool distinct or when it should be preferred over other options.

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?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the sliding window approach and that it returns theoretical vs. measured values, which adds some context. However, it doesn't describe what the output looks like, whether this is a read-only analysis or has side effects, performance characteristics, or error conditions. For a statistical analysis tool with 3 parameters, this leaves significant behavioral gaps.

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 - just one sentence in Chinese that efficiently states the core functionality. It's front-loaded with the main purpose and doesn't waste words. However, the brevity comes at the cost of completeness, as noted in other dimensions. Every word earns its place, but more content might be needed for full 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?

Given the complexity of statistical analysis with 3 parameters, no annotations, and no output schema, the description is incomplete. While it states what the tool does at a high level, it doesn't explain the significance of the analysis, what 'theoretical vs. measured values' means in practice, or how to interpret results. For a tool that presumably returns complex statistical data, this leaves the agent with insufficient context to use it effectively.

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 three parameters thoroughly with descriptions, enums, defaults, and constraints. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions '滑窗' (sliding window) which relates to the 'sequence_length' parameter concept, but doesn't provide additional semantic context. Baseline 3 is appropriate when schema does the heavy lifting.

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: analyzing sequential number occurrence probability using sliding windows and returning theoretical vs. measured values. It specifies the verb ('分析' - analyze) and resource ('号码连续出现概率' - sequential number occurrence probability), making it distinct from siblings like 'analyze_numbers' or 'predict_lottery'. However, it doesn't explicitly differentiate from 'backtest_lottery' which might involve similar statistical 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention when this analysis is appropriate, what problems it solves, or how it differs from sibling tools like 'analyze_numbers' or 'backtest_lottery'. The agent receives no contextual usage instructions beyond the basic purpose statement.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool evaluates prediction accuracy through backtesting, implying it's a read-only analysis tool, but doesn't specify what the evaluation outputs (e.g., metrics like accuracy scores), whether it modifies data, requires specific permissions, or has performance constraints. For a tool with no annotation coverage, this leaves significant behavioral gaps.

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 short phrases ('回测预测算法，评估预测准确性'), making it front-loaded and efficient. Every word contributes to the purpose, with no redundant information. However, it could be slightly more structured by explicitly separating the action from the goal, but it remains highly readable and to the point.

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 (backtesting with parameters like window_size and step), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., accuracy metrics, plots, or summaries), how results are formatted, or any limitations (e.g., computational cost). For a backtesting tool with multiple parameters and no structured output documentation, this leaves too much unspecified for effective agent use.

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 all parameters (lottery_type, window_size, step). The description adds no additional meaning beyond the schema, such as explaining how parameters interact (e.g., window_size defines training data, step controls backtest increments) or providing usage examples. With high schema coverage, the baseline score of 3 is appropriate, as the schema adequately documents parameters without extra help from the 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 '回测预测算法，评估预测准确性' (Backtest prediction algorithm, evaluate prediction accuracy) states a general purpose but lacks specificity about what resource it operates on. It mentions 'prediction algorithm' but doesn't clarify if this is about lottery predictions specifically or what exactly is being backtested. While it distinguishes from siblings like 'predict_lottery' by focusing on evaluation rather than prediction, it remains somewhat vague about the exact scope.

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 doesn't mention prerequisites (e.g., needing historical data), exclusions, or comparisons to sibling tools like 'predict_lottery' (for making predictions) or 'get_historical_data' (for data retrieval). Without such context, an agent must infer usage from the tool name and parameters 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?

With no annotations, the description carries full burden but only states the action without behavioral details. It doesn't disclose side effects (e.g., overwrites local data), permissions needed, rate limits, or error handling. '强制' (force) hints at urgency but lacks specifics on what makes it forceful.

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?

Single sentence, front-loaded with the core action, zero waste. Efficiently conveys the tool's purpose without unnecessary elaboration.

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 mutation tool (implied by 'sync' to database) with no annotations and no output schema, the description is incomplete. It lacks critical context: what data is synced, how conflicts are handled, success/failure indicators, and comparison to similar 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 description coverage is 100%, so parameters are fully documented in the schema. The description adds no extra meaning beyond implying 'latest data' relates to 'periods', but doesn't clarify semantics like what '同步期数' (sync periods) entails operationally.

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 ('强制同步' - force sync) and the resource ('指定彩票类型的最新数据到本地数据库' - specified lottery type's latest data to local database). It distinguishes from siblings like 'sync_lottery_data' by emphasizing 'force' and 'latest data', though not explicitly contrasting them.

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 like 'sync_lottery_data' or other data-fetching siblings (e.g., 'get_latest_ssq'). The description implies it's for syncing latest data but doesn't specify scenarios, prerequisites, 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?

No annotations are provided, so the description carries full burden. It mentions '推荐' (recommendations), implying advisory output, but lacks details on behavioral traits such as whether results are deterministic, if there are rate limits, or how randomness is seeded. For a tool with no annotation coverage, this is a significant gap in transparency.

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 a single, efficient sentence with zero waste. It's front-loaded and appropriately sized for the tool's complexity, making it easy to parse without unnecessary elaboration.

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 low complexity (2 parameters, 100% schema coverage, no output schema), the description is minimally adequate. It states the purpose but lacks context on usage, behavioral details, or output format. Without annotations or output schema, more completeness would be beneficial, but it meets a basic threshold.

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 schema description coverage is 100%, with clear descriptions for both parameters (lottery_type and count). The description adds no additional meaning beyond the schema, such as explaining how lottery_type affects number generation or what '组数' (groups) entails. Baseline 3 is appropriate as the schema does the heavy lifting.

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 as '生成随机彩票号码推荐' (generate random lottery number recommendations), which specifies the verb (generate) and resource (lottery numbers). It distinguishes from siblings like analyze_numbers or predict_lottery by focusing on random generation rather than analysis or prediction, though it doesn't explicitly name these alternatives.

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 doesn't mention scenarios where random generation is appropriate (e.g., for quick picks, entertainment) or when to prefer sibling tools like analyze_numbers for data-driven insights or predict_lottery for forecasts.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool gets statistical information but doesn't describe what 'statistical information' entails, whether it's read-only, if it requires permissions, or any side effects. This leaves significant gaps in understanding the tool's behavior beyond the basic action.

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 a single, efficient sentence in Chinese that directly states the tool's purpose without any unnecessary words. It's front-loaded and appropriately sized for a no-parameter tool, making it highly concise 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'statistical information' includes, the format of the output, or any behavioral traits like safety or performance. For a tool with no structured support, this minimal description leaves too much unspecified for effective use.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here, earning a baseline score of 4 as it doesn't need to compensate for any schema gaps.

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 '获取本地数据库统计信息' clearly states the action ('获取' meaning 'get') and resource ('本地数据库统计信息' meaning 'local database statistical information'), providing a specific purpose. However, it doesn't explicitly differentiate from sibling tools like 'get_historical_data' or 'get_latest_*' tools, which might also retrieve data but from different sources or with different scopes.

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 doesn't mention context, prerequisites, or exclusions, leaving the agent to infer usage based on the name alone without explicit comparison to sibling tools like 'get_historical_data' or data retrieval 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?

With no annotations provided, the description carries the full burden of behavioral disclosure but provides minimal information. It states what the tool does but doesn't describe important behavioral aspects like: whether this is a read-only operation, potential rate limits, authentication requirements, data freshness, error conditions, or what format the historical data returns. For a tool that presumably accesses potentially large datasets, this lack of behavioral context is a significant gap.

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 - a single Chinese sentence that directly states the tool's function with zero wasted words. It's front-loaded with the core purpose and appropriately sized for what it communicates, though it could benefit from additional context given the complexity of the domain and sibling tools.

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 context of multiple specialized sibling tools, no annotations, and no output schema, the description is incomplete. It doesn't explain what 'historical data' includes (draw dates, winning numbers, prize amounts?), how far back data goes, whether there are access limitations, or how this tool differs from the various 'get_latest_*' tools. For a data retrieval tool in a complex lottery analysis system, more contextual information would be helpful.

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 mentions '指定彩票类型' (specified lottery types) which aligns with the 'lottery_type' parameter, but adds no additional semantic context beyond what's already in the schema. With 100% schema description coverage that clearly documents both parameters (including enum values for lottery_type and range/default for periods), the description doesn't provide meaningful extra information about parameter usage, constraints, or implications.

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: '获取指定彩票类型的历史开奖数据' (Get historical lottery draw data for specified lottery types). It specifies both the verb ('获取' - get) and resource ('历史开奖数据' - historical draw data), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_latest_ssq' or 'get_latest_3d', which appear to fetch only the latest data rather than historical periods.

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. With multiple sibling tools for different lottery types (e.g., 'get_latest_ssq', 'get_latest_3d') and analytical tools (e.g., 'analyze_numbers', 'predict_lottery'), there's no indication of when this historical data retrieval tool is preferred over those other options or what specific use cases it serves.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't add any behavioral context such as whether it's a read-only operation, if it requires authentication, rate limits, or what the output format might be. This is a significant gap for a tool with no annotation coverage.

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 a single, efficient sentence in Chinese that directly states the tool's purpose without any wasted words. It's front-loaded and appropriately sized for a simple tool with no parameters, making it highly concise 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 tool's simplicity (0 parameters, no output schema, no annotations), the description is complete enough to understand the basic purpose. However, it lacks details on behavioral traits and usage guidelines, which are needed for full context, especially with no annotations to compensate. It's adequate but has clear 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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter information, which is appropriate here, and it doesn't contradict the schema. Baseline is 4 for zero parameters, as it adequately handles the lack of inputs.

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 ('获取' meaning 'get') and resource ('福彩3D最新开奖结果' meaning 'latest lottery draw results for 3D'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_latest_kl8' or 'get_latest_ssq', which appear to be similar tools for different lottery types, so it misses full sibling differentiation.

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 doesn't mention any context, exclusions, or comparisons to sibling tools such as 'get_historical_data' or 'analyze_numbers', leaving the agent with no usage instructions beyond the basic 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states what the tool does ('get latest results') without revealing any behavioral traits, such as whether it's a read-only operation, if it requires authentication, rate limits, or what the output format might be. This leaves significant gaps in understanding how the tool behaves.

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 a single, clear sentence in Chinese that directly states the tool's purpose without any unnecessary words. It is front-loaded and efficient, making it easy for an agent to parse quickly. Every word earns its place, achieving optimal conciseness.

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 (0 parameters, no output schema, no annotations), the description is minimal. While it states the purpose, it lacks context about behavioral aspects, output format, or how it fits among siblings. For a tool that likely returns data, more details on what 'latest results' entail would enhance completeness, especially without annotations or output 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?

The input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description does not add any parameter information, which is appropriate since there are no parameters to explain. In such cases, the baseline score is 4, as the description need not compensate for missing schema details.

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: '获取快乐8最新开奖结果' translates to 'Get the latest Happy 8 lottery draw results.' It specifies the verb ('get') and resource ('latest Happy 8 lottery draw results'), making the action clear. However, it does not explicitly differentiate from sibling tools like 'get_latest_3d' or 'get_latest_ssq,' which likely serve similar functions for different lottery types, so it falls short of a perfect 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 no guidance on when to use this tool versus alternatives. It does not mention any context, prerequisites, or exclusions, such as when to choose this over 'get_historical_data' for past results or 'analyze_numbers' for analysis. With no usage instructions, the agent must infer based on tool names 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states what the tool does (get latest results) without detailing behavioral traits such as whether it requires authentication, has rate limits, returns structured data, or handles errors. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 a single, clear sentence: '获取七乐彩最新开奖结果.' It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a simple tool with no parameters. Every part of the description earns its place by directly stating the tool's function.

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 (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks context on output format, error handling, or integration with sibling tools. For a basic read operation, it meets the minimum viable standard but leaves gaps in completeness that could hinder effective use by an agent.

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 tool has 0 parameters, and the schema description coverage is 100%, so there is no need for parameter details in the description. The description correctly omits parameter information, which is appropriate. A baseline score of 4 is given since no parameters exist, and the description does not mislead or add unnecessary complexity.

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: '获取七乐彩最新开奖结果' translates to 'Get the latest Qilecai lottery draw results.' It specifies the action (get) and resource (latest Qilecai results), making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like get_latest_3d or get_latest_ssq, which serve similar functions for other lottery types, so it falls short of a perfect 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 no guidance on when to use this tool versus alternatives. It does not mention scenarios for its use, prerequisites, or comparisons to sibling tools such as get_historical_data for past results or analyze_numbers for analysis. Without any usage context, the agent must infer when this tool is appropriate based on the name 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves latest results, implying a read-only operation, but doesn't mention any behavioral traits such as data freshness, error handling, rate limits, authentication needs, or response format. This leaves significant gaps for an agent to understand how to use it effectively.

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 a single, efficient sentence: '获取双色球最新开奖结果'. It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a simple tool with no parameters. Every part of the sentence contributes directly to understanding the tool's function.

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 low complexity (0 parameters, no annotations, no output schema), the description is minimally adequate. It states what the tool does but lacks details on behavior, usage context, or output format. For a simple read operation, this might suffice, but it doesn't provide enough context for an agent to fully leverage the tool without trial and error.

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 tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics beyond what the schema provides. A baseline score of 4 is appropriate as the description doesn't introduce confusion about parameters that don't exist.

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: '获取双色球最新开奖结果' (Get the latest SSQ lottery draw results). It specifies both the action ('获取' - get) and the resource ('双色球最新开奖结果' - latest SSQ lottery draw results). However, it doesn't explicitly differentiate from siblings like 'get_latest_3d' or 'get_latest_kl8' beyond the SSQ-specific reference.

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 doesn't mention when to use it (e.g., for SSQ specifically vs. other lottery types like 3D or KL8), when not to use it (e.g., for historical data), or suggest alternatives like 'get_historical_data' for past results. Usage is implied by the tool name 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool generates predictions based on historical data, but doesn't describe what the predictions look like (e.g., format, structure), whether it's a read-only operation, potential rate limits, accuracy claims, or how it interacts with data sources. For a prediction tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 a single, efficient sentence: '预测彩票号码，基于历史数据生成预测结果'. It's front-loaded with the core purpose and avoids unnecessary words. However, it could be slightly more structured by separating the prediction action from the data source for clarity.

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 (a prediction tool with 4 parameters) and lack of annotations and output schema, the description is incomplete. It doesn't explain the prediction output format, accuracy, data freshness, or how it differs from sibling tools. For a tool that generates predictions based on historical data, more context is needed to guide effective use.

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 all parameters well-documented in the schema itself (e.g., 'lottery_type' with enum values, 'method' with only 'rule', 'count' with range 1-20, 'strategy' with enum options). The description adds no additional meaning beyond what's in the schema—it doesn't explain what 'rule' method entails, what the strategies mean, or how parameters interact. Baseline 3 is appropriate when the schema does the heavy lifting.

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: '预测彩票号码，基于历史数据生成预测结果' (Predict lottery numbers, generate prediction results based on historical data). It specifies the verb ('预测' - predict) and resource ('彩票号码' - lottery numbers), and mentions the data source ('历史数据' - historical data). However, it doesn't explicitly differentiate from sibling tools like 'generate_random_numbers' or 'analyze_numbers' which might have overlapping 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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, when-not-to-use scenarios, or comparisons with sibling tools like 'generate_random_numbers' (which might generate numbers without historical data) or 'backtest_lottery' (which might evaluate predictions). The agent must infer usage solely from the tool name and parameters.

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?

With no annotations provided, the description carries full burden but is minimal. It states it syncs data to a local database, implying a write operation, but doesn't disclose behavioral traits like whether it overwrites existing data, requires specific permissions, handles errors, or has rate limits. It mentions '最新数据' (latest data) but doesn't clarify how 'latest' is determined or if it's incremental.

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 a single, efficient sentence in Chinese that directly states the tool's function. It's front-loaded with the core action and resource, with no wasted words. However, it could be slightly more structured by explicitly mentioning parameters or context.

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 annotations and no output schema, the description is incomplete for a write operation tool. It doesn't explain what 'sync' entails (e.g., overwrite, merge), what happens on success/failure, or the return value. For a tool that modifies a local database, more behavioral context is needed to guide safe 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 description coverage is 100%, so the schema fully documents both parameters ('lottery_type' with enum values and 'periods' with range/default). The description adds no additional meaning beyond implying 'periods' relates to syncing multiple periods of data, but this is already clear from the schema's '同步期数' (sync periods) description. Baseline 3 is appropriate as the schema does the heavy lifting.

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 ('同步' meaning 'sync') and the resource ('指定彩票类型的最新数据' meaning 'specified lottery type's latest data'), with a specific destination ('到本地数据库' meaning 'to local database'). It distinguishes from siblings like 'get_historical_data' (read-only) and 'force_sync_data' (likely a forced variant), but doesn't explicitly differentiate from 'force_sync_data' in the description itself.

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. It doesn't mention when-not-to-use scenarios, prerequisites, or compare with siblings like 'force_sync_data' or 'get_historical_data'. The description implies it's for syncing latest data, but lacks context on timing, frequency, or conflicts.

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