TrendRadar

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. It describes the tool's function (aggregation with deduplication), output format (JSON with specific fields), and default behaviors (e.g., date_range defaults to today). However, it doesn't mention potential side effects, rate limits, authentication needs, or error conditions, which are important for a tool with multiple parameters and 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 well-structured with clear sections (purpose, Args, Returns, Examples) and uses bullet points for readability. Every sentence adds value, though the Chinese-to-English translation creates minor redundancy in the purpose statement. It could be slightly more concise in the opening lines but remains efficient overall.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (5 parameters, aggregation logic) and the presence of an output schema (which handles return values), the description is quite complete. It explains the tool's purpose, all parameters, return format, and provides examples. The main gap is lack of behavioral warnings (since no annotations exist), but otherwise it covers most contextual needs adequately.

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 provides excellent parameter semantics beyond the input schema, which has 0% description coverage. Each parameter (date_range, platforms, similarity_threshold, limit, include_url) is clearly explained with meaning, format examples, defaults, and constraints (e.g., similarity_threshold range 0.3-1.0). This fully compensates for the schema's lack of descriptions and adds significant value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: '跨平台新闻聚合 - 对相似新闻进行去重合并' (cross-platform news aggregation - deduplicate and merge similar news). It specifies the verb (aggregate), resource (news), and scope (across platforms with deduplication). However, it doesn't explicitly differentiate from sibling tools like 'find_related_news' or 'search_news', which prevents 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 implies usage context through examples and parameter explanations (e.g., '不指定则查询今天' - if not specified, query today), but lacks explicit guidance on when to use this tool versus alternatives like 'find_related_news' or 'get_latest_news'. It provides some operational context but no clear when/when-not statements or named 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return format ('JSON format data insights analysis results') and provides parameter-specific constraints (e.g., date_range must be object format, not integer). However, it doesn't cover important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or what happens with invalid parameters.

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 well-structured with clear sections (Args, Returns, Examples) and uses bullet points effectively. While somewhat lengthy due to detailed parameter documentation, every sentence earns its place by adding necessary information. The front-loaded purpose statement is clear, though the Chinese title adds minor redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (5 parameters, 3 distinct analysis modes) and 0% schema description coverage, the description does an excellent job of explaining parameter usage and relationships. The presence of an output schema means the description doesn't need to detail return values. However, without annotations, it could better address behavioral aspects like error conditions or performance characteristics.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by providing comprehensive parameter documentation. It explains each parameter's purpose, lists valid values for insight_type with descriptions, specifies format requirements for date_range with examples, indicates which parameters apply to which modes, and provides default values. This adds substantial meaning beyond the bare 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 this is a 'unified data insights analysis tool' that 'integrates multiple data analysis modes' and lists three specific insight types. It provides a clear verb ('analyze') and resource ('data insights'), though it doesn't explicitly differentiate from sibling tools like analyze_sentiment or analyze_topic_trend.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage through parameter documentation (e.g., 'topic' is applicable to platform_compare mode, min_frequency and top_n are for keyword_cooccur mode). However, it doesn't explicitly state when to choose this tool over alternatives like analyze_topic_trend or compare_periods, nor does it provide exclusion criteria or prerequisites.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it mentions title deduplication ('会对标题去重'), token-saving considerations ('节省token'), and default values for parameters. It also hints at output format ('JSON格式的分析结果'). However, it lacks details on rate limits, authentication needs, or error handling.

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 well-structured and front-loaded with the core purpose, followed by usage advice, parameter details, return information, and an example. Every sentence adds value without redundancy, and the bullet-point format for parameters enhances readability while maintaining brevity.

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 (6 parameters, no annotations, but has output schema), the description is highly complete. It covers purpose, usage guidance, parameter semantics, and output format. The presence of an output schema means the description doesn't need to detail return values, and it adequately addresses the gaps left by the lack of annotations and low schema coverage.

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 0%, so the description must fully compensate. It does so excellently by explaining all 6 parameters in detail: purpose, format, defaults, constraints (e.g., '最大100'), and practical implications (e.g., '节省token'). This adds significant meaning beyond the bare schema, making parameter usage clear.

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: '分析新闻的情感倾向和热度趋势' (analyze sentiment and heat trends of news). It specifies the resource (news) and the specific analysis performed (sentiment and heat trends), distinguishing it from siblings like 'analyze_topic_trend' or 'get_trending_topics' which might focus on different aspects of news 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 clear context for usage with the suggestion to call 'resolve_date_range' for natural language dates, which is helpful guidance. However, it does not explicitly state when to use this tool versus alternatives like 'analyze_topic_trend' or 'aggregate_news', leaving some ambiguity about sibling differentiation.

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. It describes the tool's multi-mode behavior and mentions JSON output format, but lacks critical behavioral details: whether this is a read-only operation, computational cost, rate limits, authentication requirements, or what happens with invalid parameters. The description adds some behavioral context but leaves significant gaps for a tool with 8 parameters.

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 well-structured with clear sections (overview, advice, args, returns, examples) and uses bullet points effectively. While comprehensive, some sentences could be more concise (e.g., the opening line could be tighter). Overall, it's appropriately sized for an 8-parameter tool with multiple modes.

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 (8 parameters, multiple analysis modes, no annotations) and the presence of an output schema (which covers return values), the description is reasonably complete. It explains parameters thoroughly, provides usage guidance, and includes examples. The main gap is lack of behavioral transparency details that would be important for a multi-mode analysis tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description compensates excellently by providing detailed parameter semantics beyond the bare schema. It explains each parameter's purpose, lists analysis_type options with descriptions, specifies defaults, clarifies which parameters apply to which modes, and provides format examples. This adds substantial value beyond the minimal 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 performs 'topic trend analysis' with 'integrated trend analysis modes' and lists specific analysis types (trend, lifecycle, viral, predict). It distinguishes from siblings like 'get_trending_topics' by focusing on analysis rather than listing. However, it doesn't explicitly differentiate from 'analyze_data_insights' or 'compare_periods' 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 explicit guidance to call 'resolve_date_range' first when using natural language dates, which is helpful context. It also implies usage through the analysis_type parameter options, suggesting when to use different modes. However, it doesn't explicitly state when NOT to use this tool or mention alternatives among siblings like 'analyze_data_insights'.

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. It explains that the tool compares local and GitHub remote versions, checks both TrendRadar and MCP Server, supports an optional proxy for GitHub access, and returns JSON with comparison and update-needed status. This is reasonably transparent, though it does not explicitly state side-effect-free behavior or network failure characteristics.

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 well-structured with Purpose, Args, Returns, and Examples sections. It is compact, front-loaded with the main purpose, and every section contributes useful information without padding or repetition.

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 tool with one optional parameter and an output schema available, the description is complete enough to invoke correctly. It explains the return format, the optional argument, and provides two concrete call examples. Nothing essential is missing for correct 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?

The input schema has 0% description coverage, so the description must compensate. The Args section fully explains proxy_url as an optional proxy URL to access GitHub, provides a concrete example, and the schema supplies the default null. This adds clear meaning beyond the bare type definition.

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 and resource: it checks version updates by comparing local versions with GitHub remote versions, covering both TrendRadar and MCP Server. This makes it immediately distinguishable from sibling tools like sync_from_remote or get_system_status.

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 intended use is implied by the purpose and examples, but the description does not explicitly state when to prefer this tool over alternatives or when not to use it. There is no mention of sibling tools or exclusion cases, so guidance is only inferred.

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 must carry behavioral disclosure. It explains that compare_type selects different analyses (overview, topic_shift, platform_activity), that platforms filters sources, and that the result is JSON with period and type fields. It stops short of discussing failure modes, rate limits, or explicit read-only guarantees.

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 organized with clear sections (purpose, use cases, arguments, returns, examples) and front-loads the core purpose. Despite covering six parameters, every sentence contributes useful information without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a six-parameter analytical tool with no annotations but an output schema, the description is complete: all parameters are specified, supported values are listed, return shape is summarized, and three usage examples are provided. Nothing necessary to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must fully document parameters. It does: period1/period2 accept either object ranges or presets, topic is optional, compare_type enumerates all three values with meanings, platforms provides a concrete example, and top_n has its default. Examples reinforce the parameter formats.

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 opens with '时期对比分析 - 比较两个时间段的新闻数据', which names a specific verb (compare) and resource (news data across two periods). The use-case bullets further distinguish it from single-period or trend-only siblings by focusing on dual-period comparisons.

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?

A dedicated '使用场景' section lists concrete scenarios such as comparing this week vs last week and analyzing topic heat differences across periods. It gives clear when-to-use context but does not explicitly name excluded alternatives or when-not-to-use conditions.

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 and does well. It discloses key behavioral traits: supports both current and historical data, returns results sorted by similarity, includes token-saving option (include_url defaulting to False), and specifies default values for optional parameters. It doesn't mention rate limits, authentication needs, or potential errors, but covers core operational behavior adequately for a read-only tool.

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 efficiently structured and front-loaded: purpose statement first, followed by organized parameter explanations, return format, and examples. Every sentence adds value—no redundancy or fluff. The bilingual presentation (Chinese with English parameter names) is consistent and doesn't hinder clarity. It's appropriately sized for a tool with multiple parameters.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 5 parameters, 0% schema coverage, no annotations, but with output schema present, the description is complete. It covers purpose, all parameter semantics, return format (JSON list sorted by similarity), and provides examples. The output schema handles return value details, so the description doesn't need to duplicate that. It gives the agent everything needed 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?

Given 0% schema description coverage, the description fully compensates by explaining all 5 parameters in detail. It clarifies: reference_title as the target news title (full or partial), date_range options (including presets and custom range format), threshold as similarity score with default and range, limit as result count with default, and include_url as boolean for token conservation. This adds substantial meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: '查找与指定新闻标题相关的其他新闻' (find news related to a specified news title). It specifies the verb ('查找' - find) and resource ('相关新闻' - related news), and distinguishes it from siblings like 'search_news' (which likely searches by keywords) or 'get_latest_news' (which retrieves latest news without relation). The inclusion of '支持当天和历史数据' (supports current day and historical data) further clarifies 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 clear context for usage: it's for finding related news based on a reference title, with support for date filtering. However, it doesn't explicitly state when NOT to use this tool or name specific alternatives among siblings (e.g., 'search_news' for keyword-based searches or 'get_news_by_date' for date-based retrieval without relation). The examples help illustrate usage but lack explicit exclusion 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?

No annotations are provided, so the description carries full burden. It mentions the tool generates reports and returns JSON with Markdown content, but doesn't disclose important behavioral traits like whether this is a read-only operation, if it requires specific permissions, potential rate limits, processing time, or what '热点摘要' (hotspot summary) specifically entails. The description adds minimal behavioral context beyond basic functionality.

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 appropriately sized but not optimally structured. The purpose statement is clear, but the parameter documentation is embedded in the description rather than separated, and the formatting with asterisks and colons is somewhat inconsistent. Every sentence adds value, but the organization could be more front-loaded with the core 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 2 parameters with 0% schema coverage but has an output schema, the description provides adequate parameter documentation but lacks context about the tool's behavioral characteristics. The output schema existence means the description doesn't need to detail return values, but for a report generation tool with no annotations, more information about what constitutes a '热点摘要' (hotspot summary) and when to use it would be beneficial.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must compensate for the schema's lack of documentation. It provides detailed information about both parameters: report_type options (daily/weekly) and date_range format/constraints with examples. This adds significant semantic value beyond what the bare schema provides, though it doesn't explain the relationship between report_type and date_range or when date_range is required.

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 '自动生成热点摘要报告' (automatically generate hotspot summary reports) with '每日/每周' (daily/weekly) scope, which is a specific verb+resource combination. However, it doesn't explicitly distinguish this from sibling tools like 'analyze_topic_trend' or 'get_trending_topics', 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 like 'analyze_topic_trend' or 'get_trending_topics'. It mentions the report_type parameter (daily/weekly) but doesn't explain the context for choosing between them or when this tool is appropriate versus other analysis tools in the sibling list.

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 behavioral disclosure burden. It clearly conveys a read-only, informational operation ('获取', '返回'), describes the JSON return format, and enumerates the detailed behavior across channels. It does not explicitly state 'read-only' or discuss side effects, but the non-mutating nature is strongly implied.

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 long but highly structured and information-dense. It front-loads the purpose, then organizes channel differences, arguments, return type, and examples into clearly labeled sections. Every sentence contributes actionable information, and the per-channel breakdown is directly useful for the tool's 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?

An output schema exists, and the description still supplies essential context: optional behavior, valid channel IDs, return format, and usage examples. Given the tool's simple single-parameter interface, nothing an agent needs to select and invoke it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must fully compensate. It defines the channel parameter, lists all valid values (feishu, dingtalk, wework, telegram, email, ntfy, bark, slack, generic_webhook), explains that omitting it returns all channel policies, and gives concrete usage examples. This exceeds what the minimal schema alone would provide.

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 a specific verb and resource: '获取通知渠道的格式化策略指南' (get notification channel formatting policy guide), and explicitly states it returns Markdown features, formatting restrictions, and best-practice prompt suggestions per channel. This clearly differentiates it from sibling tools like send_notification and get_notification_channels.

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 says to use this tool before calling send_notification to understand the target channel's format requirements. It provides clear context for when it is relevant, though it does not explicitly discuss when not to use it or name alternatives beyond its relationship to send_notification.

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 behavioral disclosure burden. It clearly indicates a read-only 'get' operation and mentions the JSON return format, but does not address potential authentication requirements, whether the config reflects live or cached state, or any other side effects. Sufficient for a simple getter, but not richly transparent.

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 and well-structured with a front-loaded purpose and clear Args/Returns sections. It contains no fluff, though it slightly repeats the default value already present in the schema.

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 tool with one optional parameter and an output schema, the description is nearly complete. It documents all section choices and the return type. The only minor gap is not clarifying what 'current' means (e.g., live system state vs. persisted config), but this is not critical for a config getter.

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 only provides a default value and type for `section`, while the description adds all valid values: 'all', 'crawler', 'push', 'keywords', and 'weights'. Since schema description coverage is 0%, this description fully compensates by giving the agent the exact enumeration needed to call the tool correctly.

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 gets the current system configuration, with a specific resource (config) and verb (获取/get). It lists distinct config sections, which helps differentiate it from siblings like get_system_status or get_storage_status, though it does not explicitly name 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 explains how to use the `section` parameter and its allowed values, but it does not provide guidance on when to use this tool versus the many sibling tools. No exclusions or alternative tool references are given, leaving usage context to inference.

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 the behavioral burden. It discloses return format, default limit, platform handling, include_url token-saving behavior, and explicit display expectations for the agent. It does not cover error behavior or data freshness guarantees, but the read-only nature is clear from the get verb.

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 well-structured with Args, Returns, and display guidance sections. The main purpose is front-loaded, and each section earns its place, though the display-suggestion block is slightly beyond tool invocation semantics.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read tool with three optional parameters and an output schema, the description is largely complete: it covers parameter behavior, defaults, return format, and agent-facing display policy. It could still mention ordering of results or what news fields are returned, but the output schema likely covers those.

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 0%, and the description fully compensates by explaining all three parameters: platforms, limit with default and max, and include_url with its token-saving rationale. This is exactly the semantic content the schema lacks.

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 a clear verb and resource: it fetches the latest batch of crawled news data to quickly understand current hotspots. It is distinguishable from siblings like get_latest_rss and get_news_by_date, though it does not explicitly name 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 is given about when to use this tool versus alternatives such as search_news, get_news_by_date, or get_trending_topics. The only usage-related context is the display suggestion block, which addresses how to present results rather than when to select this 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?

No annotations are provided, so the description carries the burden. It discloses storage separation, time-flow display, default/maximum values, and the token-saving effect of include_summary. As a read-only fetch tool, this is adequate, though it does not mention error or authentication 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 compact and front-loaded, with a clear purpose, structured Args, Returns, and Examples sections. Minor redundancy like 支持多日查询 repeating the days parameter details is acceptable but slightly unnecessary.

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?

All four parameters are documented with defaults and limits, return format is specified as JSON, and examples demonstrate typical calls. The presence of an output schema means detailed return-field documentation is unnecessary, making this complete for a read-only RSS fetching tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description fully compensates by explaining every parameter: feeds with ID examples, days with range and default, limit with cap, and include_summary with its purpose. This goes far beyond the bare 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 opens with a specific verb and resource: 获取最新的 RSS 订阅数据, and further clarifies that RSS data is stored separately from hot-list news and displayed as a time stream. This makes it easy to distinguish from siblings like get_latest_news and search_rss.

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?

It gives a clear use case: retrieving recent content from specific RSS sources, and notes that RSS data is separate from hot-list news. However, it does not explicitly name alternatives such as search_rss or state when not to use this tool, so exclusions are missing.

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 and does well by disclosing key behavioral traits: it explains the return format ('JSON格式的新闻列表'), includes performance considerations ('节省token' for token saving with include_url default), and specifies operational limits ('默认50，最大1000'). It doesn't mention rate limits, authentication needs, or error handling, but covers more than basics for a read operation.

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 well-structured with a purpose statement, detailed Args section, and Returns section—all in a compact format. Every sentence earns its place by explaining functionality or parameters. It could be slightly more front-loaded by moving the purpose statement earlier, but overall it's efficient with minimal waste.

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 4 parameters, 0% schema coverage, no annotations, but an output schema exists, the description is largely complete. It thoroughly documents all parameters and their usage, explains the return format, and provides context for the tool's purpose. The output schema means it doesn't need to detail return values, but it could mention sibling tools or error cases for a perfect score.

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 0%, so the description must fully compensate—and it does excellently. For all 4 parameters, it provides clear semantics: date_range formats with examples, platforms as ID lists with examples, limit defaults and max, and include_url purpose and default. This adds substantial meaning beyond the bare schema, fully documenting parameter usage.

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 '获取指定日期的新闻数据' (get news data for specified dates) with the specific use case '用于历史数据分析和对比' (for historical data analysis and comparison). It distinguishes from siblings like 'get_latest_news' (which presumably gets current news) by focusing on historical/date-specific retrieval. However, it doesn't explicitly contrast with 'search_news' or 'find_related_news', keeping it from 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 implies usage context through '用于历史数据分析和对比' (for historical data analysis and comparison), suggesting this tool is for retrospective analysis rather than real-time monitoring. However, it provides no explicit guidance on when to use this versus alternatives like 'search_news' or 'get_latest_news', nor does it mention any prerequisites or exclusions. The usage context is helpful but incomplete.

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 behavioral disclosure burden. It does disclose that the tool reads config.yaml and .env and returns a JSON status with configuration source, which is useful. However, it does not explicitly state that the operation is read-only, makes no external calls, or sends no notifications, which would be valuable given the sibling send_notification.

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 purpose and key details are front-loaded, with the Returns and Examples sections adding clarity without excessive verbosity. Listing nine channels is necessary context, and the structure is easy to scan. Minor redundancy exists because the return format is already captured by the output schema, but it does not hurt usability.

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 what the tool returns, which config files are inspected, the list of supported channels, and a usage example. Since the tool has no parameters and an output schema exists, this is nearly complete. The main missing element is explicit guidance about how this tool relates to sibling notification 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?

The input schema has zero properties and 100% schema coverage, so there is no parameter detail for the description to add. The description includes an example invocation `get_notification_channels()` that confirms no arguments are required, satisfying the baseline for a zero-parameter tool.

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 opens with a specific verb and resource: “获取所有已配置的通知渠道及其状态” (get all configured notification channels and their status). It further clarifies scope by listing 9 supported channels and the config sources checked. It does not explicitly differentiate from sibling tools like get_channel_format_guide, so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies a usage context by mentioning config.yaml and .env detection, but it never states when to use this tool versus alternatives such as send_notification or get_channel_format_guide. There are no explicit exclusions or conditions that would help an agent decide between 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?

No annotations are provided, and the description conveys that this is a read-only status view through '查看' and details of generated statistics. However, it does not explicitly state side-effect-free behavior, whether data is cached, or how it behaves when no feeds exist, so the burden is only partially met.

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 structured and front-loaded with purpose, followed by a compact Returns list and an example. The opening line '获取 RSS 源状态信息' is mildly redundant with the name and second sentence, but overall there is no wasted bulk.

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 no-parameter status tool, the description is largely complete: it states scope, return structure, and an example invocation. It could be more complete by explicitly distinguishing when to use this over sibling tools like get_system_status or list_available_dates.

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 takes zero parameters, and the example get_rss_feeds_status() confirms no arguments are required. With schema coverage at 100% and no params, the description adds no parameter semantics but none are 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?

The description uses a clear verb ('获取/查看') and a specific resource (RSS feeds status and their data statistics), and the Returns section defines exactly what is included. It differentiates from siblings like get_system_status/get_storage_status by scoping to configured RSS sources and their stats.

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?

It gives clear context: use this to view currently configured RSS sources and their data statistics. It does not explicitly name alternative tools or provide when-not-to-use conditions, but the context is specific enough.

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?

无注释提供，描述承担了行为披露的负担。描述了返回内容和格式（JSON状态信息），但未明确说明操作是否只读、是否会触发副作用或需要权限。不过“查看”和“获取”暗示了非破坏性，基本行为是清晰的。

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?

描述简洁高效，第一句即点明核心功能，随后补充返回内容，每句话都有价值，没有冗余信息。

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?

工具无参数、有输出模式，描述已涵盖返回格式和主要内容。对于这种简单查询工具，没有遗漏关键信息，描述足以让代理正确调用。

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?

该工具没有参数，输入模式为空，因此描述无需补充参数语义。根据规则，0参数时基线得分为4。

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?

描述明确使用了具体动词“获取/查看”，资源是“存储配置和状态”，并具体说明包含本地/远程存储状态和拉取配置。与兄弟工具如get_rss_feeds_status、get_system_status在名称和描述上都能清晰区分。

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?

描述隐含了用途（查看存储状态），但没有明确说明何时使用此工具而非其他工具，也没有提及替代方案或排除条件。虽然功能自明，但缺少显式的使用情境指导。

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 burden of explaining behavior. It indicates a read-only operation through '获取' and '返回', and lists what data is returned. However, it does not explicitly state that the operation is non-destructive, or disclose any side effects, auth requirements, or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and front-loaded, but somewhat repetitive: '返回系统版本、数据统计、缓存状态等信息' and 'Returns: JSON格式的系统状态信息' overlap. Given that an output schema exists, the final 'Returns' line adds marginal value and could be trimmed.

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?

This is a simple parameterless status tool with an output schema, and the description covers what the status includes and the return format. However, it lacks guidance on how this tool relates to overlapping siblings and does not compensate for the absence of annotations with additional behavioral context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters and schema coverage is 100%, so no parameter documentation is needed. The description adds no parameter semantics, but the baseline for a parameterless tool is 4, which 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 names a specific verb and resource: '获取系统运行状态和健康检查信息' (get system running status and health check info) and lists concrete contents (system version, data statistics, cache status). This is clear, though it does not explicitly differentiate itself from siblings like get_storage_status or check_version beyond the broad 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?

There is no guidance about when to use this tool versus the many related siblings such as get_rss_feeds_status, get_storage_status, get_current_config, or check_version. The description implies general system status retrieval but provides no conditions, exclusions, or alternative suggestions.

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. It explains the two extraction modes, references the config/frequency_words.txt source, and states that the return is a JSON frequency list. It does not discuss side effects, but for a getter-style statistics tool the disclosed behavior is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with Args, Returns, and Examples sections. Every sentence adds value, the main purpose is front-loaded, and the examples are concise illustrations rather than repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with three optional parameters and no annotations, the description is complete: it defines all parameters, their defaults, mode semantics, extraction sources, return format, and usage examples. Nothing an agent needs to call the tool correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema provides no descriptions at all, yet the description documents every parameter: top_n with its default, mode with both allowed values and their meanings, and extract_mode with both options plus the config file dependency. Examples reinforce the parameter combinations, fully compensating for the 0% schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: retrieving hot-topic frequency statistics, and the examples make the purpose concrete. It does not explicitly distinguish itself from siblings such as analyze_topic_trend, so it misses the top score, but the verb+resource combination is specific.

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 gives clear context by explaining the mode options and showing concrete examples for preset keywords and auto extraction. It does not explicitly state when not to use this tool or name alternatives, so the guidance is strong but not fully exclusionary.

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 are absent, so the description carries the behavioral disclosure burden. It discloses that the tool reads local/remote storage, can compare both, and returns JSON date lists, which is adequate for a non-destructive listing tool. However, it does not mention date format, empty-result behavior, or network/error implications.

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 well structured with summary, Args, Returns, and Examples sections, making it easy to scan. There is minor redundancy between '列出本地/远程可用的日期范围' and '查看本地和远程存储中有哪些日期的数据可用', but no wasteful content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with one optional parameter and no required fields, the description covers purpose, source values, return shape, and examples; an output schema also exists. It could add exact date range format or behavior when no data is available, but nothing essential is missing for correct 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?

The schema only says source is a string with default 'both' and has 0% description coverage. The description compensates by enumerating valid values local/remote/both, stating the default, and giving examples. There is no ambiguity about how to use the only 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?

The first line names a specific operation (列出) and resource (local/remote available date ranges), and the description expands on distinct scopes. It stands apart from siblings like get_news_by_date or resolve_date_range because it focuses on availability of dates in storage.

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 explains how to use the source parameter and what each value does, but it does not explicitly state when to use this tool instead of siblings such as resolve_date_range or get_news_by_date. Usage is implied through '查看本地和远程存储中有哪些日期的数据可用', but no exclusions or alternatives are named.

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 the full burden, and it does so well. It discloses the underlying Jina AI Reader service, automatic noise removal, the free-tier 100 RPM limit, built-in 5-second rate control, and the fact that paywalled/login-walled pages may not be fully retrievable.

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 well organized with sections for purpose, use cases, workflow, arguments, return format, and limitations. Every section adds practical information for an agent, and the primary purpose is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the core return value (JSON containing Markdown), the expected usage flow, rate limits, and failure caveats. For a small two-parameter tool, this is sufficient for an agent to select and call it 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 description coverage is 0%, but the description compensates fully. It specifies that url is required and must start with http:// or https://, gives timeout semantics with default 30 and max 60, and includes a concrete example call.

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 a specific verb and resource: reading the content of a given URL and converting it to LLM-friendly Markdown. It clearly differentiates from search-oriented siblings by focusing on fetching and cleaning page content rather than discovering or aggregating news.

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 gives explicit context: use after search_news(include_url=True), and for reading news正文, getting article details, or analyzing content. It does not explicitly contrast with read_articles_batch, so it lacks a direct exclusion for multi-URL cases, but the intended workflow is clear.

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

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

With no annotations present, the description bears the full burden of behavior disclosure and succeeds: it reveals automatic 5-second spacing for rate-limit compliance, a hard cap of 5 articles with excess skipped, per-article failure isolation, expected duration, and the nature of the JSON return. This is substantial valuable context beyond the schema.

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 well-organized with sections for Args, Returns, Examples, and Notes, making it scannable. It loses a point for redundancy: the 5-article limit and 5-second interval are repeated in the opening sentence, the following paragraph, and the Notes section, but the overall size remains reasonable.

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 moderate complexity, two parameters, no annotations, and the presence of an output schema, the description covers every practical need: selection workflow, limits, timing, timeout, partial failure behavior, and return shape. An agent can correctly invoke this tool without further information.

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 0%, but the description fully compensates: urls is explained as a required list of article links with a maximum of 5, and timeout is defined as per-request timeout in seconds with default 30. This adds semantic meaning the bare input schema lacks.

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 a specific verb and resource: '批量读取多篇文章内容（最多 5 篇，间隔 5 秒）'. It clearly differentiates from the sibling read_article by emphasizing batch processing, and even provides a workflow where it follows search_news, making its role 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 gives a concrete '典型使用流程' with an explicit before-step: first search_news(include_url=True) to get URLs, then read_articles_batch(urls=[...]). It does not explicitly contrast with read_article or list exclusions, but the workflow and batch scope make the primary use case clear.

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

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

With no annotations, the description carries the full burden and does well: it discloses server-side computation using precise current time, consistency across AI models, supported expression categories, and the exact return JSON shape. It does not discuss failure behavior or timezone assumptions, but it provides substantial behavioral context beyond the schema.

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 longer than minimal, but it is well-structured with headers, bullet lists, and examples, and each section adds value. The workflow and duplicate examples are somewhat repetitive, but the structure is scannable and front-loaded with the recommended-priority 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?

For a single-parameter helper tool with no annotations but an output schema, the description is complete: it covers why the tool exists, when to call it, how to call it, supported inputs, return format, and worked examples chaining into other tools. Nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must fully explain the parameter, and it does. It documents the single 'expression' parameter with categorized supported formats, examples, and dynamic 'last N days' support, adding clear meaning beyond the bare 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 resolves natural language date expressions into a standard date range using a specific verb and resource. It distinguishes itself by explaining this is the recommended first call before date-consuming tools like analyze_sentiment, and the scope is precise.

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 recommends prioritizing this tool when users express dates naturally and provides a step-by-step workflow with concrete examples. It does not explicitly state when not to use it or name alternative date-related tools, but the usage context is strong and 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?

With no annotations provided, the description carries the full burden. It describes the tool's unified search capability, date handling recommendation, and return format (JSON with hot list and optional RSS results). However, it doesn't mention rate limits, authentication requirements, error conditions, or whether this is a read-only operation versus something that might trigger background processes.

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 well-structured with clear sections (purpose, recommendation, args, returns, examples). While comprehensive, it's appropriately sized for a 10-parameter tool. Some sentences could be more concise, but overall it's efficiently organized with zero wasted content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (10 parameters, 0% schema coverage) and presence of an output schema, the description provides excellent parameter documentation and clear purpose. It could benefit from more behavioral context (rate limits, permissions) and explicit sibling tool differentiation, but covers the essential usage information well.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage for 10 parameters, the description provides excellent compensation. It documents all parameters with clear explanations, default values, format examples, and mode-specific behaviors (like threshold only applying to fuzzy mode). This adds substantial value beyond the bare 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 this is a 'unified search interface' that 'supports multiple search modes' and can 'search hot lists and RSS simultaneously.' It specifies the verb (search) and resource (news), though it doesn't explicitly differentiate from sibling tools like 'search_rss' or 'get_latest_news' beyond mentioning its unified nature.

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 guidance to 'use natural language dates, first call resolve_date_range to get precise date range' which is helpful context. It also mentions the tool's unified nature (hot lists and RSS), but doesn't explicitly state when to use this versus alternatives like 'search_rss' or 'get_latest_news'.

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. It covers feed scoping behavior, default and maximum days, limit default, include_summary default, and the JSON return format. It does not mention sorting, matching semantics, or error behavior, but these are relatively minor for a search tool.

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 well-structured with a one-line summary, an Args block, Returns, and Examples. Every section earns its place, there is no filler, and the most important scoping information is front-loaded.

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 five-parameter search tool with no annotations, the description covers parameter semantics, defaults, return type, and provides two concrete invocation examples. It omits details like valid feed identifiers and sort order, but these are largely discoverable from sibling tools and do not prevent correct 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?

The schema has 0% description coverage, and the description fully compensates by explaining every parameter: keyword is required, feeds has an example and default behavior, days has default/max, limit has default, and include_summary has default. This adds significant meaning beyond the bare 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 a specific action: searching RSS subscription data for articles containing a keyword. It is unambiguous about the resource (RSS 订阅数据) but does not explicitly distinguish itself from sibling tools like search_news or get_latest_rss, so it stops short of full 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 gives useful behavioral context such as 'search all RSS feeds when feeds is not specified' and includes examples, which imply typical usage. However, it does not explicitly state when to prefer this tool over alternatives or when not to use it, leaving some routing decisions to the agent.

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. It does this well by detailing how markdown is adapted per channel (e.g., Telegram converts ** to <b>, Slack converts ** to *), stating that channels default to all configured channels, and noting that the return is JSON with per-channel status. It could be more transparent about failure behavior and rate limits, but the given detail is substantial.

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 long but well-structured with sections: purpose, per-channel formatting details, a helpful tip, args, return value, and examples. Every section earns its place, and the most important information is front-loaded. The per-channel list is dense but directly useful for predicting tool behavior.

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 multi-channel formatting and zero schema coverage, the description is remarkably complete. It explains what the tool does, how it transforms content for each channel, what parameters are accepted, what the return value looks like, and gives examples. The mention of get_channel_format_guide also connects it to related tooling. No critical information needed to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for all parameter meaning. It does so thoroughly: message is marked as required markdown content, title has a default of 'TrendRadar 通知', and channels lists all valid values. Examples further clarify usage. This fully bridges the schema gap.

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 opens with a specific verb and resource: '向已配置的通知渠道发送消息' (send messages to configured notification channels). It clearly distinguishes itself from sibling tools like get_channel_format_guide and get_notification_channels by focusing on the sending action rather than retrieval or guidance.

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 gives clear context: it sends markdown-formatted messages to channels, optionally specifying channels, and explicitly recommends calling get_channel_format_guide before sending for optimal formatting. It does not explicitly state when not to use this tool, but the purpose is distinct enough among the sibling 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 must carry the behavioral disclosure burden, and it largely does. It explains that dates already present locally are skipped (skipped_dates), failed dates are reported with errors, and the operation requires specific S3 configuration. It does not explicitly warn about network load or whether it modifies existing files beyond skipping, but the skip behavior implies non-destructive sync.

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 well-structured with clear sections: overview, Args, Returns, Examples, and Note. Every section contributes necessary information — parameter semantics, return schema, usage examples, and configuration requirements — with no redundant filler. The purpose sentence is front-loaded.

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 single-parameter tool, the description covers everything needed to invoke it correctly: parameter meaning and default, return format with all fields, configuration requirements, and usage examples. The output schema exists, but the description already details the return structure sufficiently, and the sibling context shows this is a standalone sync operation.

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 only defines 'days' as an integer with default 7 and 0% description coverage. The tool description fully compensates by explaining the meaning ('pull data from the last N days'), enumerating common values (0, 7, 30) with concrete interpretations, and providing examples for both default and explicit usage.

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 opens with a specific verb and resource: '从远程存储拉取数据到本地' (pull data from remote storage to local), making the tool's core action unmistakable. It also situates the tool in the MCP Server workflow (crawlers store to remote cloud storage, server pulls for local analysis), which clearly distinguishes it from siblings like trigger_crawl, get_storage_status, or list_available_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 gives a clear usage context: pulling crawler data from remote cloud storage (e.g., Cloudflare R2) into local for analysis. It does not explicitly name alternative tools or state when-not-to-use conditions, but the scenario and prerequisites (config file or S3 environment variables) are clear enough for an agent to select it.

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 the disclosure burden and does a good job: it reveals that this is a manual/mutating crawl action, that persistence is optional via save_to_local, that include_url=False saves tokens, and that the return is JSON task status with success/failure platform lists. It does not cover rate limits, duration, or whether the crawl may overwrite existing data, but it provides materially more than a bare mutation label.

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 and well-structured: a one-line summary, labeled Args with defaults/meaning, a Returns note, and two concrete examples. No redundant filler appears, and the key scoping behavior (default all platforms) is front-loaded.

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 three-parameter tool with an output schema and clear examples, the description covers invocation, defaults, and return shape sufficiently. It could be even more complete by noting whether the crawl runs synchronously/asynchronously and how invalid platform IDs are handled, but nothing an agent strictly needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must document parameters, and it does: platforms is explained with domain examples and a default ('use all platforms'), save_to_local is tied to the output directory, and include_url is explained with a token-saving rationale. This adds real semantic meaning beyond the raw JSON 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 opening line states a specific verb and resource: '手动触发一次爬取任务' (manually trigger a crawl task), with optional persistence. This clearly identifies it as the direct crawl-trigger action and distinguishes it from the read/analysis tools among its siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage by explaining default behavior (all platforms when platforms is unspecified) and providing two examples, but it never explicitly says when to choose this tool over alternatives. No exclusions or 'use X instead' guidance is given, though the manual-trigger wording makes the basic intent reasonably inferable.

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