TrendRadar

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v6.10.0
Disambiguation4/5
Most tools have clearly distinct purposes, with detailed descriptions helping differentiate them. However, search_news (which can include RSS) and search_rss overlap, and the three analyze_* tools could be confusing. Overall, boundaries are mostly clear.
Naming Consistency5/5
All tool names follow a consistent snake_case verb_noun pattern, including get_*, analyze_*, search_*, and read_*. No mixed conventions or vague names like 'process' or 'do_thing'.
Tool Count2/5
At 27 tools, this exceeds the 25-tool threshold for score 2 on the calibration scale. There are many status/config tools (get_rss_feeds_status, get_system_status, get_current_config, get_storage_status, check_version, list_available_dates) that could be consolidated. The count feels heavy for the apparent scope.
Completeness4/5
The tool set covers data ingestion (trigger_crawl, sync_from_remote), retrieval (news, RSS, articles), search, analysis (sentiment, trends, comparison), reporting, and notification. Missing are management/update tools for configuration or feeds, but these are likely handled by config files. Core workflows have no dead ends.
Average 4.3/5 across 27 of 27 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It adds useful context about the return content (system version, data stats, cache status) and format (JSON), but does not disclose whether the operation is read-only, any side effects, or permission requirements. The verb '获取' implies a read operation, but this is implicit rather than explicit.
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 brief, but it repeats the concept of returning information twice: once in the first sentence listing items, and again in the 'Returns:' line showing JSON format. This slight redundancy is minor and the overall structure is clear and front-loaded with the main purpose.
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 simplicity (zero parameters, output schema exists), the description is reasonably complete. It explains what the tool does and what it returns. However, it lacks any behavioral or usage context, so it does not fully guide the agent on when to choose this tool over its siblings, which is a notable gap.
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 parameters since none exist, and the input schema is empty, making the description fully adequate for parameter semantics.
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 retrieves system running status and health check information, listing specific content such as system version, data statistics, and cache status. However, it does not explicitly distinguish itself from sibling tools like get_current_config or check_version, so it loses a point for lack of differentiation.
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?
There is no guidance on when to use this tool versus alternatives such as get_current_config or get_storage_status. The description does not include any context about suitable scenarios or exclusions, providing no usage direction beyond the basic purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses the tool's multi-mode behavior, JSON return format, and date-range resolution prerequisite. It does not mention permissions, side effects, error behavior, or result structure, but the read-only nature of an analysis tool is reasonably inferable.
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 brief summary, a conditional recommendation, parameter definitions, return type, and examples. Each section earns its place and key information is 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 eight parameters, no annotations, and an output schema, the description covers all parameter semantics, the return format, and a date-resolution prerequisite, with examples. It could add result interpretation or error-handling details, but the existing explanation and output schema make it sufficiently 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%, and the description fully compensates by documenting all eight parameters, including analysis_type choices, mode-specific thresholds, defaults, and examples. This adds rich meaning 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 identifies the tool as a unified topic trend analysis tool integrating multiple analysis modes (trend, lifecycle, viral, predict). It states a specific resource and action, but does not explicitly differentiate it from sibling analysis tools like analyze_sentiment 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides one explicit usage recommendation: call resolve_date_range first when using natural-language dates. However, it does not state when to prefer this tool over sibling analysis tools or when not to use it, so alternative selection guidance is mostly implied by the analysis_type modes.
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 burden. It discloses the return format (JSON) and contents (local/remote status and pull config), implying a read-only operation, but it does not explicitly state non-destructiveness or whether remote storage is contacted.
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 brief and well-structured, starting with the primary verb and object, followed by a compact returns note. No filler or redundant content.
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?
This is a simple zero-parameter status getter with an output schema. The description explains the core purpose and return contents, which is fully sufficient given the low complexity and existing output schema.
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 there is nothing to explain. The baseline for zero-parameter tools is 4, and the description does not introduce any parameter-related confusion.
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 retrieves storage configuration and status, specifically listing local/remote storage status and pull configuration. This makes it distinct from sibling status tools like get_system_status and get_current_config.
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 use this tool versus alternatives such as get_system_status or get_current_config. The description states what it does but offers no context for selection.
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 that the tool accesses GitHub (implying network dependency) and mentions an optional proxy. It also states the return value includes version comparisons and update necessity. However, it does not explicitly confirm that the operation is read-only or describe failure behavior, which would be helpful.
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 clear purpose statement, Args, Returns, and Examples. It is front-loaded with the key purpose and every section serves a purpose without unnecessary fluff. It is appropriately concise for a tool with one parameter.
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 version-check tool with one optional parameter, the description covers the purpose, parameter, and return format sufficiently. It mentions both components checked and provides examples. The only minor gap is not explicitly stating the tool is non-destructive, but this is strongly implied by the nature of a version check.
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 schema provides no description for proxy_url (0% coverage), so the description's Args section compensates well. It explains that proxy_url is optional, for accessing GitHub, and gives a concrete example ('http://127.0.0.1:7890'). This adds meaningful semantics beyond the bare 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 clearly states the tool's function: checking version updates for TrendRadar and MCP Server by comparing local versions against GitHub remote versions. It uses a specific verb ('检查' / check) and names the exact resources, distinguishing it from all sibling tools which are news-related.
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 implies usage: if an agent needs to determine whether updates are available for TrendRadar or MCP Server, this tool is appropriate. Examples show basic usage and with a proxy, but there is no explicit statement of when to use this tool versus alternatives, nor any exclusions. The usage context is clear but not directly articulated.
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 burden. It discloses useful behavioral details such as the date_range format requirement ('必须是对象格式，不能传递整数') and the return format (JSON with Markdown), but does not state whether the operation is read-only, has side effects, or requires any preconditions.
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 Args and Returns sections, and every line adds value. It is slightly longer than necessary but not verbose, and the critical type warning is clearly highlighted with bold and examples.
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 has only two optional parameters and an output schema, the description covers the essential invocation details: purpose, parameter formats, and return format. It lacks usage guidelines and preconditions, but overall it is sufficiently complete for correct tool 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%, so the description fully compensates. It explains report_type as 'daily/weekly', and gives date_range a detailed format, example, and a warning about the object type. This adds far more meaning than the bare schema types.
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 function: '每日/每周摘要生成器 - 自动生成热点摘要报告' (daily/weekly summary generator - automatically generates hot topic summary reports). The verb 'generates' and the resource 'summary report' are specific, and the daily/weekly scope distinguishes it from sibling tools like aggregate_news or analyze_trends.
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 implies usage through the 'daily/weekly' qualifier, but it does not explicitly state when to choose this tool over alternatives or mention any exclusions. No comparison with sibling tools is provided, so the guidance is only implicit.
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 states the return format (JSON) and available sections, which is helpful. However, it does not explicitly disclose that this is a read-only operation, nor mention any potential side effects, permissions, or behavior on invalid input. The 'get' verb implies safety, but a few words about non-mutating behavior would improve transparency.
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 concise and well-structured, with clear 'Args' and 'Returns' sections. Every line adds value: the purpose statement, the parameter values, and the return type. There is no redundant information or fluff, making it easy for an agent to parse quickly.
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?
This is a simple tool with one optional parameter and an output schema. The description covers the parameter's allowed values and the return type, which is sufficient for an agent to invoke it correctly. The presence of an output schema means return structure is already defined elsewhere, so no further detail is needed.
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 only provides a type and default for the 'section' parameter, with 0% description coverage. The description compensates fully by listing all valid values ('all', 'crawler', 'push', 'keywords', 'weights') and their meanings, along with the default. This gives the agent complete information to select 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: '获取当前系统配置' (get current system configuration). It uses a specific verb ('get') and resource ('current config'), distinguishing it from sibling tools like get_system_status and get_storage_status. The listed sections further clarify the scope.
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 provides no guidance on when to use this tool versus alternatives. It does not mention exclusions or alternative tools for related tasks (e.g., get_system_status). Usage is only implied by the tool's name and the section list, which is not sufficient for an agent to make context-aware decisions.
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 burden. It discloses that it reads from config.yaml and .env and returns JSON status, giving some behavioral context. However, it does not explicitly state that the operation is read-only or describe error handling when configuration is missing, leaving 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 structured with a main description, a returns section, and an example. It includes a useful list of the 9 supported channels, which adds value without excessive verbosity. Slightly longer than necessary due to the channel enumeration, but remains focused and 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?
Given the tool's simplicity, zero parameters, and presence of an output schema, the description adequately covers the purpose, configuration sources, and supported channels. It does not need to explain return formats since the output schema exists. The only missing context is explicit error behavior, but it is not critical for a read-only getter.
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 100%, so the schema already fully describes the input. The description adds context about the output and sources, but for no-parameter tools, a baseline of 4 is appropriate.
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 function: retrieving all configured notification channels and their status. It specifies the scope (9 channels) and the data sources (config.yaml and .env), which distinguishes it from sibling tools like get_current_config or get_system_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context by indicating it detects channels from specific configuration files and lists supported channels. However, it does not explicitly state when to use this tool over alternatives, such as when checking configuration before sending notifications. No exclusions are mentioned.
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 must carry the burden. It does disclose the output structure (JSON with available_dates, total_dates, today_feeds, generated_at), which is valuable. However, it does not mention whether the data is live or cached, nor any potential side effects or error conditions. This leaves some behavioral 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-structured: a one-line purpose statement, a clarifying sentence, a detailed Returns section, and an example. It is longer than strictly necessary but each section has purpose and the key information is front-loaded. No fluff or redundancy.
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 zero parameters and the presence of an output schema, the description provides thorough detail on return fields, which is sufficient for a simple status tool. It lacks potential error scenarios or performance notes, but for its simplicity the description is largely complete.
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 (schema coverage 100% trivially), so the baseline is 4. The description correctly shows a no-argument example invocation, and no parameter explanations are needed. The description adds no unnecessary parameter details.
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 function: '获取 RSS 源状态信息' (get RSS source status information) and elaborates with '查看当前配置的 RSS 源及其数据统计信息' (view currently configured RSS sources and their data statistics). It uses a specific verb (get) and resource (RSS feed status), distinguishing it from sibling tools like get_latest_rss or search_rss, which handle content rather than status.
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 clear context: it is used to view RSS source configuration and statistics. However, it does not explicitly state when not to use it or mention alternative tools. The example call reinforces its usage but lacks exclusionary 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 of behavioral disclosure. It explains key behaviors: optional persistence (save_to_local), token-saving behavior (include_url default False), and the return format (success/failure platform lists and news data). However, it does not disclose potential side effects like network calls, rate limits, or whether the crawl is asynchronous, which would be relevant for a trigger-type tool.
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 Args, Returns, and Examples sections. It is concise, front-loaded with the primary purpose, and every sentence adds value. The examples are helpful for quick comprehension.
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 moderate complexity and zero annotation support, the description covers the essential aspects: purpose, parameters, return format, and examples. It lacks details about expected execution time, failure handling, or how results can be retrieved after a crawl (if not saved locally), but the output schema and sibling tools mitigate some of this. The description is complete enough for common use cases.
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 description significantly enriches the bare schema by explaining each parameter's meaning and default behavior. It clarifies that 'platforms' defaults to all platforms, 'save_to_local' controls saving to the output directory, and 'include_url' saves tokens by defaulting to False. This is far more than the schema provides, making parameter understanding immediate.
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: '手动触发一次爬取任务' (manually trigger a crawl task), with a specific verb and resource. It distinguishes itself from sibling tools like get_latest_news or get_rss_feeds_status by focusing on triggering a crawl rather than reading data. The optional persistence detail further clarifies its scope.
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 gives no explicit guidance on when to use this tool versus alternatives. It does not mention any prerequisites, exclusions, or conditions that would make this tool preferable over the many sibling read-oriented tools. Usage is only implied by the tool's name and description.
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 burden of behavioral disclosure. It mentions the reliance on config/frequency_words.txt for keyword mode and describes the return format, which is useful. However, it never explicitly states that this is a read-only operation, nor does it mention any side effects, permissions, or data freshness implications, leaving room for ambiguity.
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 an opening summary, Args block, Returns block, and Examples. Each section is purposeful and adds distinct value, with no redundant filler or repetition of schema defaults. The length is appropriate for the parameter complexity.
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 that an output schema exists (so return details are covered) and the description thoroughly documents all parameters with usage examples and config file references, the tool is adequately specified. An agent can confidently invoke the tool with correct parameters and understand the expected output format.
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 no parameter descriptions (0% coverage), but the description's Args section fully compensates by explaining every parameter with defaults and meaning. It defines 'top_n', both values of 'mode', and both values of 'extract_mode', providing substantially more semantic value than 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 states the tool's purpose as retrieving trending topic statistics ('获取热点话题统计'), and the Args details clarify the two time modes and two extraction modes that shape the output. However, it does not explicitly differentiate itself from sibling tools like analyze_topic_trend or analyze_data_insights, so it is specific but not fully distinguished from alternatives.
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 clear guidance on when to use each mode, especially distinguishing between 'daily' vs 'current' and 'keywords' vs 'auto_extract'. The examples offer practical invocation patterns. It does not, however, state when to prefer this tool over sibling tools, so it stops short of explicit alternative-based 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?
With no annotations, the description carries the full burden. It discloses key behaviors such as the deduplication logic, similarity threshold semantics, and default platform/date handling. However, it does not mention whether the tool triggers live crawling, whether it mutates data, or any rate limits or prerequisites, leaving some behavioral aspects implicit.
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 title, summary, labeled Args section, Returns section, and Examples. Every section adds value, and the text is concise with no filler. The examples provide concrete usage patterns, making it easy to understand quickly.
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 five optional parameters, no annotations, and an output schema only referenced in text, the description covers the core functionality, parameter meanings, and return format. It lacks explicit usage guidance vs sibling tools and potential error conditions, but these are not critical for a read-only aggregation tool. The description is complete enough for most invocation scenarios.
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% since the input schema properties lack descriptions. The description fully compensates by explaining all five parameters: date_range, platforms, similarity_threshold, limit, and include_url, including defaults, value ranges, and examples. 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.
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 function: it aggregates cross-platform news by merging similar reports of the same event into one item, displaying cross-platform coverage and comprehensive popularity. This distinguishes it from sibling tools like search_news or get_latest_news, which do not perform deduplication or coverage analysis.
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 clear context for when to use the tool: when you need deduplicated cross-platform news coverage. It also explains default behaviors (e.g., date_range defaults to today, platforms defaults to all). However, it does not explicitly mention when not to use it or compare it to alternative sibling tools, so it lacks explicit 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 burden. It discloses several useful behaviors: limit deduplicates titles, include_url saves tokens, date_range defaults to today. Yet it lacks details on potential side effects, rate limits, or behavior on empty results, making it adequate but not thorough.
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 concise and well-structured: purpose statement first, then a usage suggestion, followed by an Args list, Returns note, and a concrete example. Every sentence adds value, and it is front-loaded with the core purpose.
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 purpose, usage suggestion, parameter semantics, and return value shape, with an output schema available for full return details. It lacks edge cases or exclusions, but for a 6-parameter optional tool, it is reasonably complete and instructive.
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 explaining every parameter with defaults and constraints (e.g., platforms default to all, limit max 100 with deduplication, date_range format). This adds significant meaning beyond the bare schema keys.
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 it analyzes news sentiment tendency and heat trend, using a specific verb and resource. It distinguishes itself from siblings like analyze_topic_trend by focusing on sentiment and heat, making the 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 includes a concrete usage suggestion for natural language dates (calling resolve_date_range first), which provides clear context for a prerequisite. However, it does not explicitly compare with alternative tools or state when not to use this tool, stopping short of a full 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, the description carries the full behavioral burden. It discloses that when 'feeds' is unspecified the tool searches all RSS sources, that 'days' has a max of 30, and that the return is a JSON list. This adds meaningful context beyond the schema, though it does not mention matching logic or potential side effects (e.g., read-only nature).
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 brief introductory sentence, a clear Args list, Returns section, and practical examples. Every sentence provides useful information without redundancy, and it is front-loaded with the core purpose.
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 parameters, return type, and examples, making it complete for a search tool with an output schema. However, it does not reference how to obtain RSS source IDs (e.g., via get_rss_feeds_status) or specify behavior like sorting, which slightly limits completeness in a complex sibling 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?
The input schema has 0% description coverage, so the description fully compensates by explaining each parameter's meaning, defaults, and constraints. For example, it clarifies 'feeds' as a list of RSS source IDs with behavior when omitted, and 'days' default and maximum. This is comprehensive and goes beyond the raw 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 clearly states the tool's purpose: '在 RSS 订阅数据中搜索包含指定关键词的文章' (search articles containing specified keywords in RSS subscription data). It uses a specific verb ('搜索'/search) and resource ('RSS 订阅数据'), distinguishing it from sibling tools like get_latest_rss (which fetches latest items) and search_news (which likely searches general news).
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 implies usage by explaining the search functionality and parameter defaults, but it does not explicitly state when to use this tool versus alternatives such as search_news or get_latest_rss. There is no mention of exclusions or preferred contexts, so guidance is only implied.
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. It discloses the return format (JSON), and importantly highlights a behavioral constraint: date_range must be an object, not an integer. It also shows defaults for min_frequency and top_n. It does not discuss auth requirements or rate limits, but for a read-only analysis tool this is acceptable.
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 Args, Returns, and Examples sections. Every sentence adds value, and the examples clarify using each insight_type. Despite its length, it is information-dense and front-loaded with the purpose statement.
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 all parameters, provides usage examples, and clarifies critical input constraints (date_range object format). While an output schema exists (as noted), the description only needs to indicate JSON return format, which it does. It is complete for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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: insight_type with enum values and descriptions, topic as optional, date_range format with example and warning, min_frequency and top_n with defaults and usage context. 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 it is a unified data insight analysis tool with three specific insight types ('platform_compare', 'platform_activity', 'keyword_cooccur'). The verb and resource are clear, but it does not explicitly differentiate from sibling analysis tools like analyze_topic_trend or analyze_sentiment, though the insight_type parameter provides distinct usage contexts.
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 clear context for when to use each insight_type, with parameter explanations and examples for each mode. It does not explicitly state when not to use the tool or list alternatives, but the three modes are well-defined and cover distinct analysis needs.
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 burden of behavioral disclosure. It explains search modes, defaults, threshold behavior, and the optional inclusion of RSS results and URLs. It also describes the return format. However, it does not mention potential limitations like rate limits, pagination, or platform-specific behavior, leaving some behavioral aspects opaque.
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 brief summary, a practical usage tip, bullet-point parameter definitions, return format, and examples. Every section adds value, and the formatting makes it easy to scan despite the large number of parameters. No redundant or fluff content.
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 high parameter count (10) and absence of annotations, the description covers all needed aspects: parameter semantics, defaults, return format, and multiple usage examples. The examples illustrate common use cases including RSS inclusion and custom date ranges. It is sufficiently complete for an agent to invoke the tool correctly without external documentation.
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 sole source of parameter meaning. It explains every parameter in detail, including valid values, defaults, and formatting for date_range, platforms, sort_by, threshold, and the search modes. This fully compensates for the lack of schema-level descriptions and adds critical nuance such as the default for threshold and the meaning of 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 clearly states a unified search interface ('统一搜索接口') that supports multiple search modes and can search both hot lists and RSS. This distinguishes it from sibling tools like search_rss (RSS-only), get_latest_news (latest news), and get_trending_topics (trending), making the purpose specific and unambiguous.
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 explicit advice to call resolve_date_range first when using natural language dates, which is a clear guideline for a common use case. However, it does not explicitly state when to prefer this tool over search_rss or other siblings, though the 'unified' nature implies it is the general-purpose search tool. This is good but not exhaustive.
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 disclosure burden. It explains parameter tradeoffs (include_url default False to save tokens) and provides explicit display behavior advice ('默认展示全部返回数据...用户说总结...'), giving agents non-obvious interaction guidance. It lacks error handling or rate limit info, but that's less critical for a read-only 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 opens with a one-sentence summary, then logically organizes Args, Returns, and display suggestions. The additional '数据展示建议' section is extra but earns its place by guiding agent behavior. Slightly verbose but well-structured.
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 simplicity (3 optional parameters, output schema present), the description covers parameter semantics, return format, and even display behavior. It is complete enough for an agent to invoke correctly and handle results appropriately.
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 has zero descriptions for its 3 parameters, but the description fully compensates by explaining each parameter in detail: 'platforms: 平台ID列表', 'limit: 返回条数限制，默认50，最大1000', 'include_url: 是否包含URL链接，默认False（节省token）'. This adds meaning far beyond the raw 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 uses a specific verb and resource: '获取最新一批爬取的新闻数据' meaning 'get the latest batch of crawled news data.' It clearly distinguishes from siblings like search_news or get_news_by_date by focusing on the latest crawled batch.
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 implies this tool is for quickly checking current hot topics ('快速了解当前热点'), which provides clear context. However, it does not explicitly mention alternatives or exclusions, so agents must infer when to use other 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains parameter defaults (e.g., date_range default 'today', limit default 50, include_url default False to save tokens), return format (JSON news list with title, platform, rank), and supported date input formats. It does not disclose potential errors or rate limits, but covers the key behavioral aspects well.
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 purpose statement followed by a clear Args section. Each parameter is explained concisely without redundancy. While not as terse as a two-sentence description, the length is justified by the complexity of the date_range formats and defaults. It is front-loaded with the purpose.
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 (multiple date formats, platform filtering, limit constraints) and the lack of annotations, the description is remarkably complete. It covers behavior, parameters, defaults, and return format, making it self-sufficient for an agent to select and invoke the tool correctly. The existence of an output schema further reduces the need to describe return details beyond what's already provided.
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 descriptions (0% coverage), so the description must fully compensate. It does so admirably by detailing each parameter's supported formats, defaults, and semantics (e.g., date_range accepts range objects, natural language, single-day strings; platforms is a list of IDs; limit supports a max of 1000; include_url controls URL inclusion to save tokens). This exceeds the schema's bare type information.
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 it retrieves news data for a specified date for historical analysis and comparison. This distinguishes it from siblings like get_latest_news and get_trending_topics by explicitly scoping to date-specific data, making the purpose unambiguous.
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 clear context ('用于历史数据分析和对比' — for historical data analysis and comparison), implying when to use this tool over date-agnostic alternatives. However, it does not explicitly name alternatives or provide exclusions, 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 full burden of behavioral disclosure. It reveals that the tool uses server-side current time for calculation, which is a key behavioral trait ensuring consistency. It also details the return JSON structure with 'success', 'expression', 'date_range', 'current_date', and 'description', plus the supported expression categories. It does not cover error handling or timezone behavior, but for a date parsing tool, it is reasonably transparent.
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 average but well-structured with clear sections (why needed, recommended flow, Args, Returns, Examples). Every section adds value: the 'why' explains the tool's raison d'être, and the examples illustrate exact usage. It is not overly verbose because the tool's behavior is inherently nuanced (supports many expression forms). A minor deduction for the extra length, but it earns a high score for effective organization.
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 with no annotations and no output schema shown, the description is remarkably complete. It explains the purpose, the exact input format, the output structure with a concrete JSON example, and provides two full AI-call workflows. It covers all necessary aspects for an agent to select and invoke the tool correctly, leaving little ambiguity about the expected behavior and result.
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 zero description for the 'expression' parameter (schema_description_coverage is 0%). The description fully compensates by listing all supported expression types (e.g., '今天', '本周', '最近7天', arbitrary dynamic expressions) and providing examples. This gives the AI agent far more semantic understanding than the bare schema string field, making the parameter interface clear and actionable.
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 function: parsing natural language date expressions into standardized date ranges ('将自然语言日期表达式解析为标准日期范围'). It uses a specific verb (parse/resolve) and resource (date expressions), and the '推荐优先调用' label further emphasizes its role. This clearly distinguishes it from siblings like get_news_by_date or analyze_trends, which operate on dates rather than resolving them.
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 explicit usage context through the '推荐使用流程' section, showing exactly when to call this tool (before other date-dependent tools like analyze_sentiment) and why (server-side time ensures consistency). It also includes worked examples. However, it does not explicitly state when NOT to use it (e.g., when a concrete date range is already available), so it misses the 'when-not/alternatives' element that would earn 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, the description carries the transparency burden. It discloses the return structure, default compare_type, and parameter behaviors (e.g., topic focus, platform filter), but does not mention edge cases like invalid periods or empty results. This is solid but not exhaustive.
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 well-structured with headers, bullet points, and examples. Every section (summary, use cases, args, returns, examples) contributes necessary information for a complex multi-mode tool, avoiding 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 (6 parameters, multiple compare types, optional nested objects) and lack of annotations, the description provides a comprehensive picture: parameter formats, compare modes, return shape, and practical examples. It is fully self-contained.
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: period1/period2 formats and presets, topic, compare_type with its values, platforms list, and top_n default. It adds meaning far beyond the loose schema types, including concrete examples.
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 verb ('比较') and resource ('两个时间段的新闻数据'), and clearly distinguishes its comparative function from sibling tools that retrieve single-period or trend-specific data. The scope is well-defined.
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 provides clear context for when to use the tool (e.g., week-over-week comparison, topic heat analysis, platform activity changes). However, it does not explicitly mention exclusions or alternative tools, so it stops 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, the description carries full responsibility for behavioral disclosure. It explains defaults (date_range defaults to today, threshold=0.5, limit=50, include_url=False), the meaning of threshold, and the return format (JSON sorted by similarity). It also notes include_url is false to save tokens, adding practical context.
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 Args, Returns, and Examples sections. Each line adds value: parameter details, default behaviors, and usage examples. It is appropriately sized for a tool with 5 parameters and no 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?
Despite having an output schema (not shown), the description independently explains the return format and sorting. It covers all parameters, defaults, and provides two concrete examples. The tool's complexity (nested date_range) is fully addressed, making it complete for an agent to invoke 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 coverage is 0%, but the description thoroughly documents every parameter. It explains reference_title, date_range with all supported formats (presets and custom objects), threshold as a 0-1 similarity cutoff, limit as count, and include_url as a token-saving toggle. Examples illustrate valid 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 clearly states the tool's function: '查找与指定新闻标题相关的其他新闻' (find other news related to a specified title). It distinguishes itself from siblings like search_news (keyword search) and get_latest_news by focusing on similarity to a reference title, and explicitly mentions support for current and historical data.
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 clear usage context: it is for finding related news based on a reference title, with optional date range and similarity threshold. It does not explicitly name alternatives or exclusions, but the examples and parameter details make the intended use unambiguous.
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. It discloses storage separation, time-flow presentation, token-saving behavior of include_summary, and maximum limits for days and limit. It does not mention potential side effects or permissions, but as a read-only getter, this is reasonably 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 well-organized with a short summary, an Args list, Returns, and Examples. Each section is purposeful, and the content is dense without redundancy. It is appropriately sized for the tool's complexity.
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 has four parameters, no annotations, and an output schema exists, the description covers all essential aspects: purpose, parameter semantics, return format, examples, and operational context (storage separation, time flow). It is highly complete and actionable.
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 has no parameter descriptions (0% coverage), but the description provides thorough semantics for all four parameters: feeds with examples and default behavior, days with default and max, limit with default and max, and include_summary with its token-saving intent. This fully compensates for the schema gap.
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 that the tool retrieves the latest RSS subscription data with multi-day query support. It explicitly differentiates RSS data from hot-list news ('RSS 数据与热榜新闻分开存储'), distinguishing it from sibling tools like get_latest_news and get_trending_topics.
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 clear context for when to use it: '适合获取特定来源的最新内容' (suitable for getting latest content from specific sources) and mentions separation from hot news. However, it does not explicitly name alternative tools or state when not to use it, lacking 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?
With no annotations, the description carries the burden of behavioral disclosure. It uses '查看' (view) and '列出' (list), implying a read-only operation, and details the 'both' source behavior that compares local and remote availability. It does not discuss edge cases like unavailable remote sources or performance, but for a listing tool this is adequate.
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 Args, Returns, and Examples sections. It is succinct, front-loads the purpose, and includes only relevant details. No redundant or extraneous content exists.
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 one simple parameter and an output schema, the description is complete. It explains the operation, parameter semantics, return format (JSON with comparison), and provides examples. The presence of an output schema means detailed return fields need not be repeated in the 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 provides only a type and default for 'source' with 0% description coverage. The description fully compensates by enumerating the allowed values ('local', 'remote', 'both'), explaining their meanings, and giving examples. This is essential and well-executed 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 clearly states the tool's function: '列出本地/远程可用的日期范围' (list available date ranges for local/remote) and '查看本地和远程存储中有哪些日期的数据可用' (view which dates have data available). This specific verb+resource combination distinguishes it from siblings like resolve_date_range or get_storage_status.
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 clear context for when to use the tool (to check available date ranges from local/remote storage) through its purpose statement. However, it does not explicitly mention alternatives or situations where this tool should be avoided, so it stops short of full when/when-not 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 takes on full disclosure burden. It reveals that the tool uses Jina AI Reader, includes a free service rate limit of 100 RPM, enforces a 5-second interval between requests, and may fail on paywalled or login-protected pages. It also states that ads and navigation noise are removed. This is substantial behavioral context, though it does not cover error handling or authentication details.
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 clear opening summary, a typical flow section, parameter explanations, a return-value note, examples, and usage notes. It is concise yet comprehensive, with every sentence adding value. The front-loaded summary makes the purpose immediately clear.
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 2 parameters, no annotations, and an output schema not shown, the description is complete. It covers the input parameters, return format (JSON with Markdown), rate limits, usage flow, examples, and potential failure cases. The presence of an output schema makes the return-value explanation sufficient, and the description fills all other gaps.
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 fully compensates by explaining both parameters: url is required and must start with http:// or https://, and timeout is the request timeout in seconds (default 30, max 60). It also marks url as required (必需). This goes beyond the raw schema and gives complete semantic guidance.
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 that the tool reads article content from a specified URL and returns LLM-friendly Markdown. It explicitly names the resource (article content) and the action (read), and differentiates itself from sibling tools such as search_news and read_articles_batch by focusing on single-URL reading for analysis.
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 an explicit typical usage flow: first use search_news to get links, then use read_article to fetch content. It also lists appropriate use cases (reading news, getting article details, analyzing content) and notes limitations (paywall pages may fail). However, it does not explicitly contrast with read_articles_batch for batch scenarios, so it falls short of a full 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 behavioral burden. It details days semantics (0, 7, 30), skipped local dates, failed-date reporting, and required S3 configuration. It does not explicitly state whether local files are overwritten or whether remote data is deleted, but the skipped-dates behavior implies a non-destructive sync.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with purpose, Args, Returns, Examples, and Note sections. Every section adds necessary information and the one-line purpose 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 tool with one parameter and an output schema, the description covers the return fields, the expected data flow, and configuration prerequisites. It is complete enough for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the description fully compensates. The Args section explains days as '拉取最近 N 天' with default 7, enumerates special values 0/7/30, and the Examples section demonstrates invocation with and without arguments.
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: '从远程存储拉取数据到本地' (pull data from remote storage to local). It clearly distinguishes this tool from siblings that check status or query data, and even states the MCP Server use case.
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 explains the intended context ('用于 MCP Server 等场景') and the data flow, giving clear context for when to use it. However, it does not explicitly mention when not to use it or name alternative 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?
With no annotations provided, the description carries the full burden of transparency. It clearly states what the tool returns (supported Markdown features, format restrictions, and formatting prompts) and describes the optional channel behavior ('不指定返回所有渠道策略'). Examples illustrate expected outputs. However, it does not disclose handling of invalid channel IDs or potential errors, which 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 well-structured with an intro, a bullet list of channel differences, Args/Returns sections, and examples. It front-loads the main purpose. However, the channel differences bullet list is quite verbose and repeats information the tool itself will return; while useful for deciding whether to call the tool, it could be trimmed to the most essential contrasts without losing value.
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 tool is simple with one optional parameter and an output schema present (as indicated by 'has_output_schema: true'). The description provides a clear return type ('JSON格式的渠道格式化策略'), mentions the optional channel behavior, and includes two examples. This is comprehensive for the tool's complexity and leaves no major gaps for the agent.
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 provides only a 'channel' parameter with anyOf string/null and no description. The tool description fully compensates by explaining the parameter's meaning ('指定渠道 ID', optional), default behavior ('不指定返回所有渠道策略'), and valid enum values (feishu, dingtalk, wework, telegram, email, ntfy, bark, slack, generic_webhook). This rich semantic addition is essential for correct 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 clearly states the tool's purpose: '获取通知渠道的格式化策略指南' (get notification channel formatting guide). It explicitly names the resource (channel formatting strategies) and the verb (get), and distinguishes itself from sibling tools like send_notification and get_notification_channels by focusing on formatting details. The bullet list of channel-specific features further clarifies its scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly instructs to '在调用 send_notification 之前使用此工具' (use this tool before calling send_notification), providing a clear when-to-use directive. It also explains the optional channel parameter and the behavior when omitted. Though it doesn't mention alternatives by name, the primary alternative (send_notification) is directly referenced, giving strong contextual 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 fully carries the burden. It discloses the rate-limiting behavior (5-second interval), the maximum limit (5 articles, excess skipped), the per-request timeout (default 30 seconds), the approximate duration for 5 articles, and the failure isolation. This is rich behavioral transparency.
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 front-loaded headline, clear sections for usage flow, arguments, returns, examples, and notes. Every sentence adds value, and it is concise for the amount of information conveyed.
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 necessary context: prerequisite tools (search_news), usage example, return format (JSON with content and status), limits (max 5, skipped excess), timing (25-30 seconds), and error handling (single failure isolation). It is complete for a tool with this complexity.
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 compensates fully: it explains 'urls' as a required list of article links (max 5) and 'timeout' as per-request timeout in seconds with default 30. It also adds the 5-second interval context, which is not in the 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 clearly states the tool's purpose with a specific verb and resource: '批量读取多篇文章内容' (batch read multiple article contents). It also specifies the scope (max 5 articles) and interval (5 seconds), which distinguishes it from the singular 'read_article' sibling.
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 clear typical usage flow: first search_news, then read_articles_batch, then AI analysis. It implies that this tool is for multiple articles, but it does not explicitly state when to use the singular 'read_article' instead. This is a clear context with no exclusions, so a 4 is appropriate.
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 takes full responsibility for behavioral disclosure. It thoroughly details markdown adaptation per channel (e.g., Slack mrkdwn conversion, Email HTML), default title, channel fallback behavior, and return format (JSON with per-channel status). No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections for action, per-channel details, usage hint, args, returns, and examples. Every sentence provides value, and the length is justified by the complexity of channel-specific formatting rules.
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 multi-channel notification tool with minimal schema (3 params, no descriptions), the description is highly complete. It covers channel-specific behaviors, defaults, return format, and examples. Since an output schema exists, full return structure documentation is unnecessary.
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 descriptions are absent (0% coverage), but the description fully compensates by documenting all three parameters: message (required), title (default), and channels (list with valid values). It adds meaning beyond the schema, including channel enum values 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 clearly states the tool sends messages to configured notification channels ('向已配置的通知渠道发送消息'), with a specific verb and resource. It distinguishes from siblings like get_channel_format_guide (formatting guidance) and get_notification_channels (channel listing).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance to call get_channel_format_guide before sending to optimize formatting, which directs the agent to a relevant alternative. The description also clarifies the default behavior (send to all channels) and includes examples, making usage context clear.
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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