trendradar-mcp

Server Quality Checklist

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Latest release: v6.10.0
Disambiguation3/5
The toolset is organized into recognizable categories, but several pairs overlap in purpose: search_news can also search RSS, making it close to search_rss; analyze_data_insights(platform_activity) and compare_periods(compare_type=platform_activity) cover similar ground; and get_latest_news vs get_news_by_date vs get_trending_topics require careful distinction. The detailed descriptions help, but an agent still has to make fine discriminations between several tool pairs.
Naming Consistency5/5
All 27 tools use consistent snake_case action_object naming: get_*, search_*, analyze_*, read_*, send_*, trigger_*, list_*, compare_*, etc. Even less standard verbs like resolve_date_range and sync_from_remote still follow the same predictable action-object pattern. This is a highly consistent and learnable naming system.
Tool Count2/5
At 27 tools, the server exceeds the 25+ threshold and places a heavy load on tool selection. Several tools could be consolidated, such as search_rss vs search_news(include_rss=True), read_article vs read_articles_batch, and the various status/config tools. The broad scope justifies more than a minimal set, but this many tools would be better split or thinned out.
Completeness4/5
The server covers the full workflow: date resolution, crawling/syncing, news/RSS retrieval and search, trend/sentiment/insight analysis, aggregation, period comparison, report generation, article reading, and notifications. Operational tools for storage, system status, and configuration are also present. Minor gaps exist mainly in non-core admin actions, such as modifying keywords or RSS subscriptions, but agents can work around these.
Average 4.2/5 across 27 of 27 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 4 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under GPL 3.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. It mentions that the tool returns JSON with Markdown content, but it does not state whether the operation is read-only, whether it triggers crawling or other side effects, or what limitations exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and well-organized with Args and Returns sections. The example and important note for date_range are useful, though the opening phrase repeats the tool name without adding much.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter tool, inputs and output format are well covered, and an output schema reduces the need to describe return values. However, missing usage guidance and side-effect disclosure make the definition incomplete for an agent choosing among many sibling analytics and reporting tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds substantial value by specifying report_type values and providing a concrete object format for date_range with an example and a warning against passing integers. However, the 'must be object' instruction conflicts slightly with the schema's anyOf allowing object, string, or null.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates daily/weekly hot-topic summary reports, with a specific verb and resource. It does not explicitly differentiate itself from similar sibling tools like aggregate_news or compare_periods, but the purpose is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. The description only documents arguments and returns, leaving the agent to infer which sibling tool is appropriate for a given task.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full transparency burden. It indicates a read-only operation through '查看' and '获取' and describes the output as JSON containing local/remote storage status and pull configuration. However, it does not disclose whether remote access is performed, failure behavior, or permission requirements, though the getter nature mitigates this somewhat.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loaded with the core purpose, then expands on what status includes. There is minor redundancy between the first and second lines, but the overall length is appropriate for a no-parameter status tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter read-only status tool, the description sufficiently covers what the tool does and what it returns. The presence of an output schema reduces the need to document return structure in detail. It lacks explicit alternative routing, but the low complexity makes this acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description does not need to explain parameter meanings, and it appropriately focuses on the return value and purpose instead.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets storage configuration and status, with an explicit verb ('获取') and resource ('存储配置和状态'), and further details local and remote storage status. It is distinct from get_system_status or get_rss_feeds_status by focusing specifically on storage, though it does not explicitly name sibling alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to choose this tool over related tools such as get_current_config, get_system_status, or sync_from_remote. The description implies use when storage status is needed, but it does not state exclusions, prerequisites, or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 retrieval operation (获取) and states that the output is JSON-formatted system status information. It does not mention auth or rate-limit implications, but for a zero-parameter status getter this is a minor gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded, with the main purpose stated immediately. There is minor redundancy between the second line and the 'Returns' line, but no significant filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless status tool with an output schema, the description is largely complete: it names the main return categories and the JSON format. It could be stronger by explicitly routing storage/RSS/version-related queries to sibling tools, but nothing critical is missing for invoking the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and schema coverage is effectively complete, so there are no parameter semantics for the description to add. The baseline of 4 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb and resource: 获取系统运行状态和健康检查信息, and lists concrete returned categories such as system version, data statistics, and cache status. It is identifiable as the general system-status tool, though it does not explicitly differentiate itself from sibling status tools like get_storage_status or check_version.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to choose this tool over its siblings, many of which also report status (get_storage_status, get_rss_feeds_status, get_current_config, check_version). There are no exclusions, prerequisites, or alternative conditions, so the usage context must be inferred from the name and first line.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It only states that results are returned as JSON; it does not disclose whether the operation is read-only, what data source it reads from, whether there are rate limits, or any side effects. For an analysis tool this is a meaningful gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized with Args, Returns, and Examples sections, with the unifying purpose front-loaded. Each parameter entry earns its place, and the examples demonstrate realistic calls across all three modes. Redundancy with schema defaults is minor.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 modes, 5 params, no annotations), the description is nearly complete: it covers every parameter, mode, format nuance, and return type, and the output schema covers return shape. It lacks only broader operational context such as data source, prerequisites, or limitations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
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: it defines the valid insight_type values, marks topic as platform_compare-specific, specifies date_range format with an example and a warning against non-object input, and gives mode-specific defaults for min_frequency and top_n. This is exactly the information an agent needs beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the tool as a unified data insight analysis tool and enumerates three distinct analysis modes (platform_compare, platform_activity, keyword_cooccur), which gives it a recognizable resource and scope. It is distinguishable from specialized sibling tools by being explicitly multi-mode, though 'data insights' remains somewhat broad.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly explains what each insight_type does and which parameters apply to which mode, providing strong internal usage context. However, it does not state when to prefer this tool over sibling alternatives such as analyze_topic_trend or analyze_sentiment, and no exclusions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It explains that the tool queries local and remote storage and returns a JSON comparison, but it does not explicitly state whether this is read-only, whether it triggers any network calls, or whether it caches results. This is adequate for a simple listing tool but leaves some ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized into summary, args, returns, and examples. There is slight redundancy between the first line and the second line, but the content is front-loaded and every section earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 adequately covers what the tool does, how to call it, and what it returns. The main gap is that it does not clarify how the 'remote' source differs behaviorally from 'local' in terms of latency or failure modes, but this is minor given the examples and return description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema only declares a string parameter with a default, and schema description coverage is 0%. The description compensates fully by enumerating the three valid values ('local', 'remote', 'both'), explaining what each does, and showing usage examples. This is exactly the semantic information an agent needs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists available date ranges from local and remote storage, using a specific verb ('列出') and resource ('本地/远程可用的日期范围'). This distinguishes it from siblings like resolve_date_range or get_news_by_date, which focus on other date-related operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the source parameter values and provides examples, but gives no explicit guidance on when to prefer this tool over alternatives such as resolve_date_range or get_storage_status. No when-to-use or when-not-to-use conditions are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does disclose useful behavior: default time window (默认 7 天), max window (最大 30 天), all-feed scope when feeds is omitted, and JSON return format. It leaves gaps on matching semantics (exact vs fuzzy, title vs full-text), behavior with zero matches, and result ordering.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-organized into purpose, Args, Returns, and Examples with the purpose front-loaded. The examples earn their place. Minor redundancy: defaults restated in Args (默认 7 天, 默认50, 默认False) duplicate what the input schema already displays.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 5-parameter tool with zero schema descriptions and no annotations, the description covers purpose, every parameter's meaning, defaults, and return format; an output schema exists so return values need not be spelled out. Missing only the source of valid feed IDs (e.g., via get_rss_feeds_status) and match semantics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the Args block must fully compensate — and it does. All five parameters (keyword, feeds, days, limit, include_summary) receive meaningful semantics, and it adds a constraint the schema lacks (days 最大 30 天) plus the all-sources default behavior for feeds.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific action — '在 RSS 订阅数据中搜索包含指定关键词的文章' — with a clear verb (search), resource (RSS subscription data), and intent. It is distinguishable from siblings like get_latest_rss by the keyword-search focus, but it does not explicitly differentiate itself from search_news, leaving some discrimination to the agent.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The Args section gives conditional guidance ('不指定时：搜索所有 RSS 源') and the Examples show plausible invocations, which implies usage context. However, it never states when to prefer search_rss over siblings like search_news or get_latest_rss, nor any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral disclosure burden. It does disclose defaults, the similarity threshold semantics, and the JSON return shape, which is useful. However, it does not mention whether the operation is read-only, whether it triggers any background work, or potential rate/error behavior, leaving gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized: a brief summary, a compact Args block, Returns, and Examples. It is concise, front-loaded, and every section earns its place without unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters, an output schema, and no annotations, the description covers purpose, all parameters, return shape, and examples. The main gaps are the underspecified date_range format and the lack of explicit usage boundaries, so it is nearly complete but not fully.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
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: every parameter gets a plain-language explanation, default value, and often a concrete range or example (e.g., similarity_threshold 0.3-1.0, platforms ['zhihu', 'weibo']). 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: cross-platform news aggregation with deduplication and merging of similar reports, using a specific verb (聚合/去重合并) and resource (news). It distinguishes behavior from sibling fetch/search tools, but it does not explicitly name an alternative, so it stops short of a full 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use case is implied by the summary and argument descriptions, but the description does not explicitly state when to use this tool instead of siblings like get_latest_news or search_news, nor does it mention when not to use it. The usage context is inferable but not direct.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 that the tool retrieves current configuration and returns JSON, which implies a read-only operation. It does not mention side effects, auth requirements, or rate limits, but for a simple getter this is acceptable minimal disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and well-structured: a one-line purpose, a clear Args block with allowed values, and a Returns block. There is no filler or redundant repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the single optional parameter, every valid value, the default, and the return type. An output schema is present, so return details are not the description's burden. A minor gap is the lack of an explicit read-only/safety statement, but the operation is clearly a getter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
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 defining 'section' and enumerating all accepted values, including the default 'all'. This adds all semantic meaning an agent needs beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as retrieving the current system configuration and enumerates the config sections available. It is distinguishable from sibling status/news tools by the explicit 'config' resource, though it does not directly contrast with any sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lists valid section values and the default, so an agent understands what it can fetch. However, it gives no explicit guidance on when to use this tool versus alternatives like get_system_status or get_storage_status; usage context is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden and does well: it defines 'daily' versus 'current' modes, explains the difference between preset keyword extraction and auto-extraction, mentions the config file dependency, and notes that auto_extract requires no preset. It could add explicit read-only/no-side-effect language, but the semantics are well covered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: a one-line summary, clear Args list, Returns statement, and concrete examples. Every section adds useful information, and the most important behavioral choices (mode and extract_mode) are front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, the return values need minimal description, and the Args/Examples fully explain the tool's expected inputs and modes. It is slightly incomplete in that it doesn't clarify how 'trending' is computed beyond frequency counts or how it relates to sibling analytics tools, but overall it gives an agent enough to call it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description is the only parameter documentation. All three parameters (top_n, mode, extract_mode) are explained with their defaults and allowed values, and examples clarify realistic combinations. This fully compensates for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The summary '获取热点话题统计' clearly identifies the tool as retrieving trending/topic frequency statistics, and the detailed Args/Examples make the resource and actions unambiguous. It does not explicitly distinguish itself from sibling tools like analyze_topic_trend or aggregate_news, but the name and description make the core purpose clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains modes and extract modes but provides no guidance about when to choose this tool over alternatives such as analyze_topic_trend, get_latest_news, or aggregate_news. Examples show common invocations, but there is no explicit when-to-use or when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses optional local saving, token-saving behavior of include_url, and the return status format. However, it does not clarify what happens to data when save_to_local is False, whether the crawl writes to system storage, or operational traits like runtime and failure behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized with Args, Returns, and Examples sections. It is concise, front-loaded with the core purpose, and every section adds practical value. The examples are useful without being redundant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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, an output schema, and no required params, the description covers all parameter semantics, defaults, and return behavior. Minor gaps remain around what 'optional persistence' means when save_to_local is False and how the tool should be selected over sibling sync tools, but overall it is sufficiently complete for invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
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 all three parameters: platforms with example values and default-all behavior, save_to_local as optional persistence, and include_url as a token-saving toggle. This adds meaning well beyond the bare boolean/array schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description states a specific verb and resource: manually triggering a crawl task, with optional persistence. It is clearly distinguished from sibling tools like get_latest_news or sync_from_remote by the concept of triggering a crawl.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 sync_from_remote or scheduled crawling. The examples show valid invocations but do not state conditions, exclusions, or when another tool should be preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden and does reasonably well: it discloses parameter defaults and limits (limit max 1000, include_url default False to save tokens), clarifies the return format as JSON, and adds agent-facing display guidance (show all data by default, only summarize on explicit request). It does not mention rate limits, error behavior, or auth, but this is a simple read-style fetch.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the primary action and then uses compact sections for Args, Returns, and display suggestions. It is slightly longer than the minimum, but every section earns its place, especially the display guidance which is directly actionable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With three fully explained parameters and an output schema present, the description gives an agent enough to invoke the tool correctly. It even adds useful presentation context. The main gap is not distinguishing this tool from several news-related siblings, but the name and opening line supply the core context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
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. It does: each parameter is explained with examples and defaults ('platforms' with ['zhihu','weibo'], 'limit' with max 1000, 'include_url' with token-saving rationale). This adds clear meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a clear verb-resource statement ('获取最新一批爬取的新闻数据', get the latest batch of crawled news data) and indicates the purpose of quickly surveying hot topics. It is clear about what the tool does, but it does not contrast with siblings like get_latest_rss, get_news_by_date, or search_news, so it only earns a 4, not a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase '快速了解当前热点' implies the tool is for a quick look at current news, and the display suggestions instruct the agent on presentation behavior. However, there is no explicit statement of when to use this tool instead of alternatives such as get_news_by_date or search_news, nor any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden, and it does well: it discloses that results are sorted by similarity, that threshold controls strictness, that include_url defaults to false to save tokens, and that omitting date_range limits results to today. It lacks details about authentication, rate limits, or empty-result behavior, but for a read-oriented news lookup these are not critical.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a short summary, Args section, Returns note, and practical examples. Every section adds value, and the most important behavioral constraints (default date behavior, threshold, token-saving flag) are front-loaded. It is detailed without being bloated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers all 5 parameters, default behaviors, return sorting, and usage examples, which is strong for a tool of this complexity. It does not explicitly discuss error handling or authentication, and it does not route the agent away from sibling tools, but with an output schema present and parameter semantics fully covered, it is close to complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description is the only source of parameter meaning. It explains reference_title, date_range with all possible formats and values, threshold semantics, limit, and include_url with its token-saving rationale. This fully compensates for the empty schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the core function: finding other news related to a specified news title, with support for both current and historical data. It uses a specific verb and resource ('查找与指定新闻标题相关的其他新闻') and is unambiguous, though it does not explicitly distinguish itself from sibling tools like search_news or get_news_by_date.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear operational context, such as default behavior (today only), date range presets, threshold semantics, and examples. However, it does not explicitly state when to prefer this tool over alternatives like search_news or analyze_topic_trend, so usage guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 clearly states that the tool reads config.yaml and .env, checks 9 notification channels, and returns JSON with per-channel configured status and source. This reveals the read-only nature and the underlying data sources, though it does not address error cases or permission requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
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 config sources, supported channels, return format, and an example. Each section is useful, though the example is somewhat redundant for a tool with no parameters. Overall it is concise and easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple zero-parameter tool with an output schema, the description is largely complete: it explains what is returned, what configuration sources are checked, and which channels are covered. It does not specify behavior when config files are missing or invalid, but those are edge cases an agent can reasonably infer. The description provides enough context for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the input schema is an empty object with 100% coverage. Per the rubric, a no-parameter tool gets a baseline of 4 because the schema already provides all necessary information. The example call confirms there are no arguments, so the description adds no parameter-related value beyond what the schema already conveys.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb '获取' and a clear resource '所有已配置的通知渠道及其状态', precisely stating what the tool does. It lists the 9 supported channels and this distinguishes it from sibling tools like send_notification and get_channel_format_guide, which focus on sending or formatting rather than configuration status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use is implied: when an agent needs to know which notification channels are configured and their status. However, the description never explicitly states when to use this tool over alternatives, nor does it mention exclusions or provide routing guidance relative to the many sibling tools. The zero-parameter signature makes the use case fairly obvious, but explicit differentiation is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral disclosure burden and largely meets it: the verb '查看' (view) implies a non-destructive read, and the return format is exhaustively documented including generation timestamp and per-feed item counts. It does not explicitly state read-only/no-side-effects or mention rate limits/pagination, but for a zero-parameter status tool the disclosed behavior is substantive and accurate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a title line, one-sentence summary, a bulleted Returns section, and a usage example. The first two lines are mildly redundant ('获取 RSS 源状态信息' vs '查看当前配置的 RSS 源及其数据统计信息'), but every other sentence earns its place by documenting the exact JSON shape an agent can expect.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, read-only status tool with an output schema already present, the description is near-complete: it documents the output schema in prose anyway, provides an example invocation, and names the key fields. The only gaps are the absence of explicit selection guidance among the 26 sibling tools and no explicit non-destructive claim, both minor given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4; there is nothing for the description to clarify at the parameter level. The example call demonstrates the no-argument invocation, which is consistent with the empty input schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb+resource pair ('获取 RSS 源状态信息' – get RSS feed status) and clarifies it is about the current configured feeds and their data statistics, not feed content. The documented return shape (available_dates, today_feeds, generated_at) clearly distinguishes it from content-retrieval siblings like get_latest_rss/search_rss and from system/storage status tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied via the example call 'get_rss_feeds_status() # 查看所有 RSS 源状态' and the return structure suggests it is meant for inspecting feed data coverage and per-feed counts. However, there is no explicit statement of when to use this tool versus alternatives such as list_available_dates, get_latest_rss, or get_system_status, and no when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral disclosure burden. It adds useful details like title deduplication, default sorting by weight, and include_url defaulting to false to save tokens, plus the return structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the purpose, then organized into Args, Returns, and Examples. Every line adds value, and the example clarifies real usage without bloat.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 6-parameter tool with no schema descriptions and no provided annotations, the description covers inputs, defaults, output shape, and a related helper tool. It is slightly incomplete only in not routing the agent away from similar analysis siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
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—and it does. Every parameter is explained with format, defaults, examples, or constraints, such as date_range format, limit maximum, and deduplication behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action and resource: analyzing news sentiment and popularity trends. It is clear but does not explicitly differentiate itself from similar analysis siblings like analyze_topic_trend or analyze_data_insights.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives practical usage context, including the recommendation to call resolve_date_range for natural-language dates and the default behavior for platforms and date ranges. It does not, however, state when to prefer this tool over related analysis tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses useful behavioral details: it checks two components, compares local versions with GitHub remotes, and returns JSON results including an update-needed flag. The words 'check', 'compare', and 'judge' imply a read-only verification operation, though it does not explicitly state that no update is performed or discuss network failure behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and well structured, with a front-loaded summary followed by Args, Returns, and Examples sections. Every section adds practical value and there is no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with an output schema present, the description is nearly complete: it explains the parameter, the return format, and provides invocation examples. Minor gaps remain around explicit sibling differentiation and external network assumptions, but the agent has enough information to call the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides no description for proxy_url beyond type and default, so the description fully compensates by stating it is optional, explaining its purpose (accessing GitHub), and giving a concrete example URL plus call examples. This gives the agent enough semantic understanding to use the parameter correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: it checks version updates for both TrendRadar and MCP Server by comparing local versions against GitHub remote versions. This is specific and distinct from the sibling tools, which focus on status, storage, dates, and news rather than version checking.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied through the description and examples, but the description never explicitly says when to choose this tool over alternatives such as get_system_status or get_storage_status. It gives examples of calling the tool but no exclusions or conditional guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well: it discloses that the tool returns JSON-formatted strategies, that omitting channel returns all channels, and it enumerates per-channel differences. It implicitly reads as a safe, non-mutating operation though it does not explicitly state 'no side effects'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections for overview, channel difference summary, Args, Returns, and Examples. The channel-by-channel breakdown is informative but slightly lengthy; still, every section adds value and the key usage guidance is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The definition fully covers purpose, usage timing, parameter values, return format, and example invocations. An output schema exists to formalize the return structure, so the description need not duplicate it. Nothing essential for an agent to call this tool correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite the schema having no description for the channel property (0% coverage), the description fully compensates: it lists all allowed values (feishu, dingtalk, wework, telegram, email, ntfy, bark, slack, generic_webhook), explains the parameter is optional, and specifies the behavior when omitted. This is richer than any schema could provide alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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 ('获取通知渠道的格式化策略指南') and clearly distinguishes this tool from siblings like get_notification_channels and send_notification. It states it returns Markdown features, format limitations, and best-practice prompts per channel, making its scope unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly instructs to use this tool '在调用 send_notification 之前' (before calling send_notification) to learn target channel formatting requirements. It stops short of naming alternatives or exclusion cases, but the primary usage context is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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, and it does well by explaining defaults ('默认值: 今天'), behavior when platforms are unspecified ('不指定则使用所有平台'), why include_url defaults to False ('节省token'), and the returned JSON content. It does not cover edge cases like empty results or sorting, but the main behavioral traits are transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and well-structured: a one-sentence purpose statement followed by Args and Returns sections. Every line adds useful information, and the date_range examples are especially practical without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
All four parameters are fully explained and the return shape is summarized, so an agent has enough to call the tool correctly. The presence of an output schema reduces the need to detail return fields further. Minor gaps such as ordering or date-range boundary behavior keep it from being a perfect 5.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the description thoroughly documents all four parameters. date_range lists four supported formats with examples, platforms gives an example list and the all-platform default, limit states both default and maximum, and include_url explains the boolean default and its purpose.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with '获取指定日期的新闻数据' (retrieve news data for a specified date), naming the exact verb, resource, and temporal scope. It further states it is intended for '历史数据分析和对比' (historical data analysis and comparison), which clearly distinguishes it from siblings like get_latest_news.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly establishes the context for use: retrieving news for a specified date range or for historical analysis and comparison. It does not explicitly name alternative tools or state when not to use the tool, but the date-based scope provides clear situational guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
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, and it does so well. It reveals the underlying Jina AI Reader service, automatic noise removal, 100 RPM limit, built-in 5-second rate control, and the limitation that paywalled pages may not be fully fetched. These are exactly the behavioral traits an agent needs to know 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is longer than minimal but well-structured with sections for use cases, workflow, Args, Returns, Examples, and Notes. The front-loaded first sentence immediately states the core function. A few phrases are somewhat redundant, such as repeating Markdown output, but overall the structure makes the information easy to scan and every major section earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with only two simple parameters and no annotations, the description is complete: it defines the input constraints, shows an example call, describes the return format, and warns about rate limits and paywall limitations. The only minor omission is not addressing the batch sibling, but that does not materially affect correct invocation of this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
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 missing parameter details—and it does thoroughly. It explains that url is required and must start with http:// or https://, and that timeout is in seconds with a default of 30 and maximum of 60. This adds meaningful semantic value beyond the bare input schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action ('读取指定 URL 的文章内容') and the return format ('LLM 友好的 Markdown'), making the tool's core purpose unambiguous. It also names relevant use cases like reading news and analyzing article content, which helps distinguish it from search-oriented siblings. However, it does not explicitly contrast itself with the sibling read_articles_batch, so cross-tool differentiation is slightly weaker than ideal.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a concrete typical workflow: use search_news(include_url=True) first, then read_article(url=链接), and follow with AI analysis. It also states suitable scenarios ('阅读新闻正文、获取文章详情、分析文章内容'). It does not mention when to avoid this tool or explicitly point to read_articles_batch for multiple URLs, but the guidance is clear enough for most agent decisions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full disclosure burden and handles it well: it explains search modes, default date behavior, threshold semantics, include_rss behavior, and output type. It doesn't mention rate limits or error conditions, but for a read-style search tool, the key behavioral traits are covered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long because 10 parameters need documentation, but every section earns its place: purpose statement, helper guidance, Args breakdown, Returns line, and illustrative examples. The structure is front-loaded and easily scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 10-parameter multi-mode search tool, the description is thorough: it covers modes, defaults, return format, platform examples, and natural-language-date handling. It stops just short of explaining when to prefer distinct siblings like search_rss or get_latest_news, and does not list available platform IDs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
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, and it does. Every parameter has a meaning, format, default, and relevant examples, including mode-specific behaviors like threshold only applying to fuzzy mode.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a concrete resource ('统一搜索接口' for news), specifies search modes, and states it can cover both hot-list and RSS data. This makes it clearly distinguishable from siblings like get_latest_news, search_rss, and get_news_by_date.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives a clear, actionable recommendation to call resolve_date_range when using natural language dates, and the '统一搜索接口' framing signals it as the general search entry point. It does not explicitly enumerate alternatives or exclusion conditions, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral transparency burden and does it well by explaining the four analysis modes, mode-specific parameters, and defaults such as spike_threshold, time_window, lookahead_hours, and confidence_threshold. It also states the return type as 'JSON格式的趋势分析结果', giving the agent a clear picture of the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a one-line summary, an actionable suggestion, a parameter list, return type, and examples. It is appropriately sized for an 8-parameter tool with multiple modes, and every section adds useful information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
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, 4 analysis modes, no annotations), the description is sufficiently complete: all parameters are explained, defaults are given, mode-specific parameters are called out, and concrete examples show real invocation patterns. An output schema exists, so the brief return description is acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
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 fully document the parameters, and it does: topic, analysis_type with all four allowed values, date_range format, granularity, spike_threshold, time_window, lookahead_hours, and confidence_threshold are all explained with defaults and mode relevance. Examples further clarify parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with '统一话题趋势分析工具 - 整合多种趋势分析模式', clearly identifying the tool as a topic trend analysis utility with multiple analysis modes. It distinguishes itself from siblings like analyze_sentiment or compare_periods by specifying trend, lifecycle, viral detection, and prediction as its core functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises calling resolve_date_range first when using natural language dates, which is a clear usage guideline tied to a sibling tool. It also documents the default behavior ('默认最近7天', '默认"day"'), but does not explicitly state when to prefer this tool over alternatives such as get_trending_topics or analyze_data_insights.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations were provided, so the description carries the behavioral burden. It clearly explains the return contract ('Returns: JSON格式的对比分析结果'), the different outputs per compare_type, and provides examples of expected usage. It does not explicitly state read-only behavior or error/empty-data handling, but the analysis framing and return-only description make side effects unlikely.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is organized into clear sections—purpose, use scenarios, Args, Returns, Examples—and each section earns its place. It is longer than average, but the parameter count and enum-like options justify that length; the key purpose is front-loaded and examples are compact.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given six parameters, no annotations, and a sparse schema, the description covers all needed invocation details: parameter formats and defaults, valid compare_type values, platform filtering, top_n, return structure, and multiple usage examples. The minor naming inconsistency between return key 'platform_comparison' and compare_type 'platform_activity' is not load-bearing because an output schema exists and return parsing is not needed to invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
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. It specifies period1/period2 object syntax and preset strings, the topic keyword's optionality, the three compare_type values with their meanings and the 'overview' default, the platforms array example, and the top_n default of 10. This goes well beyond the bare types in the JSON schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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', giving a specific verb and resource: comparing news data across two time periods. It further clarifies the dimensions involved ('热点话题、平台活跃度、新闻数量'), which distinguishes it from single-date tools like get_news_by_date or general search tools in the sibling list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The '使用场景' section explicitly lists when to use this tool: comparing week-over-week hot topics, topic heat differences, and platform activity periodicity. However, it does not mention any against alternatives or state when not to use it, so it falls just short of full guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it provides meaningful behavioral details: it returns JSON, supports multi-day queries, enforces days/limit maximums, and notes that include_summary defaults to false to save tokens. The get verb also clearly implies a read-only operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a one-line summary, a clarifying context sentence, labeled Args and Returns sections, and concrete examples. Every section adds value and helps an agent use the tool without unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given four optional parameters, no annotations, and an output schema, the description covers all invocation details: parameter semantics, defaults, bounds, return type, and usage examples. The agent has everything needed to call the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
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 as RSS source IDs with an example, days with default and max, limit with default and max, and include_summary with a default and rationale. This goes well beyond the input schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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 immediately distinguishes RSS data from hot-list news by noting they are stored separately. This prevents confusion with siblings like get_latest_news while making the operation's scope clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states this tool is suitable for getting the latest content from specific sources, giving the agent a clear context for use. However, it does not explicitly name alternatives or state when not to use it, so it falls short of full exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral transparency burden. It discloses server-side exact-time computation, consistency benefits, supported expression categories, and the complete return JSON structure including date_range, current_date, and description. It does not cover error cases or timezone behavior, but the key behavioral characteristics an agent needs are present.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but every section earns its place: a clear headline, a rationale, a recommended usage flow, an args section, a returns section, and two end-to-end examples. The structure uses headings and bullet lists to make the content scannable, and the key recommendation is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter utility tool, the description covers why to use it, how to use it, what expressions are accepted, what the output looks like, and how to chain it with downstream tools. The output schema is effectively documented through the Returns section and examples. Nothing essential for correct invocation is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema only defines expression as type string, so schema coverage is 0% and the description fully compensates. It enumerates supported natural-language categories (single day, week, month, recent N days, dynamic days), gives concrete Chinese and English examples, and shows exactly how the expression translates into the return date_range. This is far beyond basic parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb-resource statement: '将自然语言日期表达式解析为标准日期范围' (parse natural language date expressions into standard date ranges). It further clarifies the tool's role, distinguishes it from direct AI date calculation, and provides examples of the exact output. This makes the tool's purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly explains when to use the tool: whenever users provide natural language date expressions like '本周' or '最近7天', and gives a recommended call flow before invoking downstream analysis tools. It does not explicitly state when not to use it or name alternative date-handling tools, but the recommended workflow is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral disclosure burden and does so thoroughly. It explains internal adaptation per channel, exact Markdown-to-HTML/mrkdwn transformations, default behavior of sending to all channels when none are specified, default title, and the JSON result containing per-channel status. This gives the agent a strong model of side effects and return behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well structured with purpose, per-channel details, arguments, return type, and examples. It is somewhat long, but the length is largely justified by the complexity of eight different channel formatting behaviors. The Args section partially duplicates schema defaults, but it adds necessary semantic value given zero schema descriptions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is complete enough for an agent to invoke the tool correctly: it specifies required vs optional parameters, accepted channel values, default behavior, output shape, and even an example for common calls. The existence of an output schema further covers return structure, and the pointer to get_channel_format_guide fills the formatting-optimization gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
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. It defines message as Markdown content, title with its default value, and channels with the full list of allowed values plus the default of sending to all configured channels. This adds far more meaning than the bare schema types and defaults.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a clear statement of purpose: '向已配置的通知渠道发送消息' — sends messages to configured notification channels. It gives the specific verb and resource, and the detailed channel list distinguishes it from sibling read/analysis tools, none of which send notifications.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly indicates when to use the tool: whenever a notification needs to be sent to one or more configured channels. It also points the agent to get_channel_format_guide for detailed formatting strategy before sending. There are no explicitly stated exclusions, but no sibling tool offers an alternative sending path, so exclusions are less necessary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does an excellent job: it discloses the 5-second delay between requests, the 5-article cap with overflow skipped, per-request timeout behavior, and that a single failure does not affect others. It also explains the expected JSON return structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Args, Returns, Examples, Note) and front-loads the most important constraints: max 5 articles and 5-second interval. Every section adds useful information without padding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's constraints, timing, failure behavior, return format, and usage flow. Since an output schema exists, the lack of detailed return-field documentation is not a gap. The description is fully sufficient for an agent to decide when and how to invoke this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
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 both parameters. It does: urls is required, limited to 5 articles, and timeout is per-request in seconds with a default of 30. The example usage also grounds the parameter format.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('批量读取多篇文章内容') with a clear resource (文章) and scope (最多 5 篇). It also distinguishes itself from the sibling read_article by explicitly being the batch version with multi-article processing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a concrete typical workflow: first search_news, then read_articles_batch, then compare articles. It clearly implies this is for multi-article analysis, but does not explicitly contrast it with read_article for single-article cases, so exclusions are not fully articulated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It explains the days=0/7/30 semantics, skipped local dates, the complete return shape, and required S3 configuration. This gives the agent a strong behavioral model.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized into purpose, Args, Returns, Examples, and Note sections. Every section adds necessary information, and the structure makes it easy for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool, it provides everything needed to call it correctly: behavior, parameter choices, result format, and configuration prerequisites. Even with an output schema present, the practical context is valuable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must define the parameter. It thoroughly documents 'days' with default, special values, and examples, far exceeding the bare integer schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description begins 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), and explains the MCP Server scenario. This clearly distinguishes it from siblings like trigger_crawl 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.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives a concrete use case: crawlers upload to remote cloud storage and the MCP Server pulls data locally for analysis. It does not explicitly name alternatives or state when not to use this tool, so it does not achieve the top score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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