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 only says '获取版本信息' without indicating that this is a read-only, non-destructive operation, whether authentication is required, or any other behavioral traits. It does not mention side effects or return format (though output schema exists). For a simple version retrieval, the transparency is minimal and does not add meaningful context beyond the literal action.

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

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

The description is a single, concise sentence that immediately conveys the tool's purpose. It is front-loaded with the action and resource, containing no superfluous words. This is ideal for a simple tool.

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

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

Given the low complexity (no parameters, clear purpose) and the presence of an output schema that presumably defines the return structure, the description is sufficient for an agent to call the tool correctly. It does not need to explain return values because the output schema covers that. However, it lacks any mention of when this might be useful, but for a version info tool, that is not critical. Overall, it is complete for its scope.

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

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

The tool has zero parameters, and schema coverage is 100% (vacuously). The description correctly adds no parameter information because there are none. According to the rubric, a baseline of 4 is appropriate for a zero-parameter tool, and the description does not introduce any confusion about parameters.

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

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

The description states the verb '获取' (get) and the specific resource 'Zabbix API版本信息' (Zabbix API version information). It clearly identifies what the tool does and naturally distinguishes it from the sibling tools, which all deal with hosts, events, or reports rather than API version details.

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

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

No guidance is provided on when to use this tool versus alternatives, prerequisites, or exclusions. While the purpose is obvious for a version check, the description does not explicitly state that it should be used to verify API version compatibility or any context in which it is preferred over other tools. This leaves the agent without explicit usage direction.

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

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

With no annotations provided, the description carries the burden of behavioral disclosure. '查询' (query) strongly suggests a read-only operation, and the listed outputs are useful, but it does not explicitly state non-mutation, permissions, or data scope. The word '统一' is also somewhat ambiguous.

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

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

The description is a single concise sentence with no filler, and the host-scoping action is front-loaded. The compressed phrasing around '统一当前问题分类' costs a little structural clarity, so it is not a perfect 5.

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

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

The required host_identifier is obvious, all parameters are documented in the schema, and the output schema covers return values. The main gaps are lack of when-to-use guidance and some ambiguity in the aggregated outputs, but the definition is largely sufficient for correct invocation.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add meaning about time_range, trend_hours, include_oracle, or include_trends, but all parameters are already well-documented in the input schema.

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

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

The description states a clear host-scoped action with specific output dimensions: interface availability, problem classification, and key metrics. It goes beyond the name, though it does not explicitly distinguish itself from siblings like quick_status or get_problem_summary.

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

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

The description implies when to use the tool: when a host-scoped, current health/composite view is needed. However, it provides no exclusions or explicit alternatives, so an agent cannot fully compare it with host_get or quick_status.

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

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

With no annotations, the description carries the burden, and it does disclose key behavioral traits: display counts default to the scheduled push report and can be overridden per chapter. It also mentions the production-only filter tied to ZABBIX_TEST_NETWORKScars. While it doesn't explicitly state side-effect freedom, 'generate Markdown' strongly implies a non-mutating report operation.

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

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

A single, front-loaded Chinese sentence conveys the core action, the default display behavior, and the two main customization axes. There is no filler or unnecessary repetition of schema details.

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

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

Given that all 7 parameters are fully documented in the schema and an output schema exists, the description is sufficient for an agent to invoke the tool correctly. It could add a brief list of report chapters or explicit alternative routing, but the essential calling context is present.

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

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

Schema description coverage is 100%, so the baseline is 3, but the description adds meaning by tying the top_n_* parameters to 'chapter overrides' and clarifying that default counts follow the scheduled push report. This is context beyond the individual parameter descriptions.

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

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

The description opens with a specific verb-resource pair ('生成运维日报 Markdown') and makes it clear this is the daily report, distinguishing it from siblings like event_weekly_report and event_monthly_report. The output artifact and its format are unambiguous.

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

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

It provides useful context—default counts match the scheduled push report and sections/production scope can be overridden—but it never names alternatives or states conditions for choosing daily vs weekly/monthly. Usage timing is implied by the tool name rather than explicitly explained.

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

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. The description clarifies that this tool returns raw events, not business interpretations, which is a useful behavioral note. However, it doesn't disclose other behaviors like pagination details (beyond the eventid_till parameter), rate limits, or whether operations are read-only (likely safe, but not stated). Lacking annotations, more behavioral context would be expected, so a 3 is appropriate.

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

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

The description is a single concise sentence: '查询原始历史事件；业务状态解释使用问题事实明细工具。' It is appropriately short and front-loads the primary purpose, then provides a routing hint. No unnecessary words. However, it could be slightly more structured by explicitly naming the alternative tool, but overall it is efficient.

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

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

Given the tool complexity (13 parameters, no required params) and the presence of an output schema, the description provides essential context by stating the raw data nature and directing to the right sibling for interpretation. However, it lacks details on common query patterns, default behaviors (e.g., time range defaults), or how the cursor pagination works, which are not fully covered by the schema. The schema is rich but the description could compensate with more operational context, so a 3 is fair.

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

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

Schema coverage is 100%, so each parameter has a description, but many are terse (e.g., 'hostids' just says '主机ID', 'severities' says '严重级别列表'). The description does not add additional context beyond what's in the schema. For instance, it doesn't explain the relationship between 'value' and 'eventids' or clarify acceptable formats for 'time_from' beyond the schema's mention. Thus, the description adds minimal value over the schema, warranting a baseline 3.

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

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

The description states '查询原始历史事件' (query raw historical events), which clearly indicates the tool's purpose of retrieving raw event data. It also mentions that business status interpretation should use another tool ('问题事实明细工具'), which helps distinguish it from sibling tools that provide interpreted or summarized data. However, it doesn't explicitly name the sibling tool 'get_problem_fact_detail', so differentiation is partially implicit.

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

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

The description advises using a different tool for business status interpretation ('业务状态解释使用问题事实明细工具'), giving clear guidance on when NOT to use this tool. However, it doesn't elaborate on when exactly to use this tool versus other event-related siblings like event_monthly_report or event_daily_report, so the guidance is present but not exhaustive.

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

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

No annotations are provided, so the description carries the full burden. It mentions that the display counts are consistent with the scheduled push report by default, which is useful, and that sections can be overridden or production-only scope can be applied. However, it does not disclose side effects (e.g., whether it triggers a push or just returns content), return format details beyond Markdown, or any permissions or rate limits. The mutation-like behavior is not clarified.

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

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

The description is a single concise sentence that front-loads the core purpose (generate monthly report) and mentions key behaviors (default consistency, overrides, production-only). It is efficient with no waste, though it could be slightly more structured for readability.

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

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

Given the tool's complexity (8 parameters, output schema exists) and lack of annotations, the description is adequate but leaves gaps: it does not explain return value structure (though output schema exists, so that is covered), does not mention when to use versus siblings, and does not clarify potential side effects. The description covers the main customization options but lacks contextual guidance for an agent to decide between this and daily/weekly reports.

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

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

The schema description coverage is 100%, so the schema already documents all 8 parameters thoroughly, including defaults and special values for top_n parameters. The description adds little beyond that, but it does mention the ability to override sections and production-only scope, which aligns with parameters. Baseline 3 is appropriate as the description adds minimal extra semantic value beyond the schema.

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

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

The description clearly states the tool generates an operations monthly report in Markdown format, with a specific resource (event data) and scope (monthly). It distinguishes from daily/weekly siblings by the word 'monthly' in the name and mentions customizable sections and production-only filtering, which differentiates it from other report tools.

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

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

The description implies when to use this tool (for monthly report generation) and provides options like per-section override and production-only mode, but it does not explicitly mention when NOT to use it or name alternatives like event_daily_report or event_weekly_report. The context signal lists sibling tools, but the description itself lacks explicit routing.

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

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It discloses that the output is Markdown, default counts match scheduled push, and that the 'production_only' parameter excludes test networks based on configuration (ZABBIX_TEST_NETWORKS). This is meaningful behavioral context, though it doesn't detail the full report structure or any side effects, but for a report generation 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.

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

The description is a single concise sentence that front-loads the core purpose and then lists key options. It wastes no words, and every clause adds useful information. This is an example of efficient, high-value description.

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

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

Given the tool's complexity (8 parameters, all optional) and the presence of a rich output schema, the description covers the purpose, default behavior, and main options. It doesn't explain each parameter in detail, but the schema does, and the description focuses on the high-level context. A minor gap is not mentioning the report's time period explicitly in the description, but the name and default cover that. Overall, it's complete enough for an agent to call it correctly.

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

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

Schema description coverage is 100%, so the schema documents each parameter's meaning and defaults. The description adds value by contextualizing the purpose of these parameters (e.g., chapter overrides and production-only filtering) and linking to the default behavior. This goes beyond the schema by explaining why these parameters exist.

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

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

The description clearly states the tool generates a weekly operations report in Markdown for Monday to Sunday, with explicit mention of default display counts matching the scheduled push and options for chapter overrides and production-only filtering. This distinguishes it from siblings like event_daily_report and event_monthly_report by specifying the weekly scope and output format.

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

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

The description implies usage for generating weekly reports and mentions the default behavior and overrides, but it does not explicitly state when to use this tool versus daily or monthly reports. However, the weekly name and default 'last_week' strongly imply the intended use case, and the siblings are self-explanatory, so context is clear without explicit exclusions.

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

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

No annotations are provided, so the description must disclose behavioral traits. It only notes precision and token savings—performance characteristics—but does not mention read-only nature, authentication, rate limits, error behavior, or return format. This leaves the agent underinformed about operational expectations.

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

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

The description is two short clauses, front-loading the purpose and then providing usage guidance. Every word earns its place with no redundancy or padding.

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

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

For a simple one-parameter lookup with an output schema, the description is adequate. It covers purpose and usage guidance. Minor gaps (e.g., exact-match behavior, behavior when IP not found) are not critical for a tool of this simplicity, and the output schema covers return values.

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

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

The schema has 100% coverage for the single ip parameter, so the baseline is 3. The description adds the nuance of 'precise' matching, but that is already implied by the tool name and does not significantly enrich parameter semantics beyond the schema.

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

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

The description clearly states the tool queries a host precisely by IP, and explicitly contrasts it with sibling host_get as more precise and token-saving. This distinguishes it from alternatives and leaves no ambiguity about its function.

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

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

It gives an explicit usage condition ('known IP → prefer this tool') and names the alternative host_get with a comparative advantage. This is clear when-to-use guidance with an implied when-not-to-use (when IP is not known).

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

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

With no annotations, the description carries the behavioral disclosure burden. It clearly states this is a read/query operation and indicates the kind of status information returned, but it does not cover failure behavior, permissions, data freshness, or any other operational caveats. Basic transparency is present, but depth is limited.

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

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

A single compact sentence states the verb, resource, key input, and expected output dimensions with no filler. The most important information is front-loaded and every word contributes.

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

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

Given a single required parameter, 100% schema coverage, and an output schema present, the description is sufficiently complete for an agent to invoke the tool correctly. The main gap is the absence of sibling differentiation, but that is addressed under usage guidelines rather than this dimension.

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

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

Schema description coverage is 100%, so the parameter is already documented in the schema. The tool description adds no extra parameter meaning beyond what the schema provides; the schema's clarification that eventid is 'not the trigger ID' is the useful semantic and is already present.

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

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

The description uses a specific verb '查询' (query) and identifies the resource as problem event fact details, enumerating the returned dimensions: actual recovery, manual handling, and current monitoring status. It is clear but does not explicitly distinguish itself from siblings such as get_problem_summary or event_get, so it misses the top score.

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

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

The description implies when to use the tool: when you have a problem event ID and need recovery/handling/monitoring details. However, it offers no explicit guidance on when not to use it or how it compares to similar sibling tools, leaving selection to inference.

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

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

No annotations are provided, so the description carries the behavioral burden. The verb '查询' makes the read-only nature reasonably clear, and '当前' scopes the operation. However, it does not disclose additional behavioral context such as data freshness, aggregation behavior, or limitations beyond what the schema already states.

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

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

Two short sentences with no filler: the first states the purpose, the second routes historical-event queries to the correct sibling. Information is front-loaded and every word earns its place.

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

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

Given the full schema descriptions and the presence of an output schema, the description is largely complete for tool selection and invocation. The only notable gap is not mentioning get_problem_fact_detail as the counterpart for detailed current-problem facts, but this is not essential for correctness.

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

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

Schema description coverage is 100%, so each of the 4 parameters is already well documented (view, hostids, group_by, time_range). The tool description adds no parameter-specific meaning, so the baseline score of 3 is appropriate.

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

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

The description clearly states a specific action ('查询', query) and resource ('当前问题摘要', current problem summary after unified classification), distinguishing it from event_get for historical events. It does not explicitly distinguish from the sibling get_problem_fact_detail, though the word '摘要' (summary) implies a difference in granularity.

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

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

The description explicitly directs historical-event use cases to event_get ('历史事件用event_get'), giving clear when-not guidance for the most likely confusion. However, it does not offer comparative guidance for other nearby siblings such as get_problem_fact_detail or quick_status.

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

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

With no annotations, the description carries the behavioral burden; it does state that the operation is a read-only retrieval of raw historical values rather than trend summaries. It does not mention auth needs, side effects, or default behavior, but for a simple history fetch these are not major risks. This is minimally adequate but leaves some behavioral gaps.

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

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

Two sentences with no filler; the purpose is front-loaded and the second sentence routes to an alternative tool. Every word earns its place.

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

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

An output schema exists and the input schema is fully documented, so the remaining need is selection context, which the description supplies with the trend_get alternative. It could more explicitly say 'use this for short-term raw data', but the combination of description and schema is sufficient for correct invocation.

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

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

Schema description coverage is 100%, so the schema already documents every parameter with types, defaults, and value ranges (e.g., limit default 10, history data types, relative times for time_from). The description adds no parameter-specific meaning, which is acceptable because the schema carries the full load.

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

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

The description opens with a specific verb and resource: '获取监控项原始历史数据' (get raw history data for monitored items). It explicitly names the key sibling distinction, '长期趋势用trend_get', so an agent can tell it apart from related history/trend tools.

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

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

It gives an explicit routing condition: for long-term trends, use trend_get, which implies this tool is for raw/detailed history. It does not enumerate other sibling alternatives or define exactly when 'long-term' begins, but the main competing tool is handled clearly.

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

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

No annotations provided, so the description must fully disclose behavior. It mentions 'complete pagination' which hints at pagination support, but it does not state read-only status, return format, permission requirements, or whether it aggregates data. The meaning of 'precise restoration of pairing' is unclear, leaving the agent uncertain about what the tool actually returns.

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

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

The description is a single concise sentence with no filler. It is efficient but slightly cryptic due to the ambiguous 'precise restoration' phrase. It is front-loaded with the core purpose, though not overly detailed.

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

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

For a tool that queries alert history, with many sibling history tools, the description lacks essential context. It does not explain the difference from event_get or history_get, nor does it indicate the output structure (despite an output schema being noted, it is not visible). An agent would be uncertain about when and how to invoke it correctly.

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

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

Schema description coverage is 100%, with each parameter having a basic description (host ID, trigger ID, days). The tool description does not add extra semantics, such as how pagination is controlled or the relationship between days and alert retrieval. Baseline 3 applies because the schema handles the heavy lifting, but the description contributes little.

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

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

The description states it queries alert history for host and trigger with pagination and precise pairing restoration. However, the phrase 'precise restoration of pairing' is ambiguous, and it does not differentiate from sibling tools like event_get or history_get which also handle history queries. The verb 'query' and resource are clear, but the scope is vague.

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

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

No guidance on when to use this tool versus alternatives. With many sibling tools (event_get, history_get, trigger_get), the agent has no explicit direction on which tool fits which scenario. There is no mention of exclusions or conditions.

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

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

No annotations are provided, so the description carries the full burden. It mentions query modes (ID exact, name fuzzy) but doesn't disclose behavior like pagination limits, default limit of 10, whether results are sorted, or what happens with multiple filters combined. The description is minimal and doesn't add much beyond the schema.

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

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

The description is a single concise sentence in Chinese, front-loading the main purpose and then adding the routing hint. It's efficient with no wasted words. However, it could be slightly more structured with separate sentences for usage guidance.

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

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

Given 10 parameters and no annotations, the description is somewhat thin. It doesn't explain the difference between filter, search, and hostids, nor the output parameter behavior. The output schema exists, which helps, but the description doesn't clarify when to use this tool vs hostgroup_get_hosts or item_get. It's adequate but has clear gaps for a tool with this many parameters.

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

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

Schema description coverage is 100%, so the schema already documents all 10 parameters. The description adds a small amount of context by mentioning ID exact query and name fuzzy match, which maps to hostids and name parameters. However, it doesn't explain the relationship between filter, search, and hostids, or how output/selectGroups work. Baseline 3 is appropriate since schema covers everything.

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

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

The description states a clear verb and resource: '查询主机列表' (query host list), and specifies two query modes: exact ID query or name fuzzy match. It also distinguishes itself from get_host_by_ip by saying to prefer that tool when IP is known. However, it doesn't fully differentiate from other sibling tools like hostgroup_get or item_get, though the resource is clear.

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

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

The description gives explicit guidance to use get_host_by_ip when IP is known, which is a clear alternative routing. It implies this tool is for name/ID-based queries. It doesn't explicitly state when NOT to use this tool beyond the IP case, but the guidance provided is useful and specific.

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

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

No annotations are provided, so the description carries the full behavioral burden. It only says '查询' (query), which implies a read operation, but it does not disclose pagination behavior, return format, filtering semantics beyond schema, or any potential side effects.

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

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

The description is a single, clear sentence with no wasted words. It is appropriately front-loaded and easy to parse, though it offers very little information beyond the bare purpose.

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

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

The tool is simple with only two optional parameters and an output schema available, so the minimal description is not severely inadequate. However, it lacks usage context and behavioral details, making it only minimally complete for reliable agent selection and invocation.

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

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

Schema description coverage is 100%, with both 'name' and 'limit' parameters already described in the schema. The tool description adds no additional parameter-level meaning, so the baseline 3 applies.

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

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

The description '查询主机群组列表' clearly states a specific action (query) and resource (host group list). It is not a tautology and is distinguishable from sibling tools like hostgroup_get_hosts, though it does not explicitly differentiate itself.

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

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

No guidance is given about when to use this tool versus alternatives such as hostgroup_get_hosts or host_get. The description simply states what the tool does without any context, exclusions, or preferred scenarios.

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

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

No annotations are provided, so the description carries the full burden of behavioral transparency. The term '查询' (query) implies a read-only operation accurately, and the prerequisite about obtaining groupid is disclosed. However, it does not describe side effects, authorization needs, pagination behavior beyond the schema default, or any special result characteristics beyond '主机列表'.

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

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

The description is one sentence, front-loads the primary purpose, and then provides the necessary prerequisite. Every word earns its place, with no redundancy or filler. This is an appropriately concise and well-structured description for a simple query tool.

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

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

The tool has only two parameters, an output schema, and a straightforward read operation. The description supplies the key prerequisite and the core purpose. It is slightly incomplete in not addressing how to choose this tool over sibling tools like host_get, but otherwise it is sufficient given the low complexity and existing schema.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds meaningful context for the groupids parameter by stating it must be obtained via hostgroup_get, but it does not expand on the limit parameter or provide additional semantics beyond the schema. The schema already documents defaults and types adequately.

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

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

The description states a specific verb and resource: '查询群组内的主机列表' (query host list within a group). It clearly identifies the operation and scope, but does not explicitly differentiate it from sibling tools like host_get or hostgroup_get, so it is clear but lacks explicit sibling distinction.

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

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

The description provides a useful prerequisite: '需先通过hostgroup_get获取groupid' (must first get the groupid via hostgroup_get). However, it does not explicitly state when to use this tool versus alternatives such as host_get, nor does it provide exclusions or alternative routing. The guidance is implied but not comprehensive.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states what data is returned (software asset info) but does not disclose any behavioral traits such as whether the query is read-only, whether it requires specific permissions, whether it returns partial data if the host is not found, or any rate limits. For a read-like query tool, the lack of explicit read-only or error behavior disclosure is a gap.

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

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

The description is a single concise sentence that front-loads the main purpose and lists the data categories. It is efficient and has no wasted words. It could be slightly improved by adding usage guidance, but for what it is, it is well-structured and appropriately sized.

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

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

Given the tool has one parameter, a clear schema, and an output schema, the description is mostly complete for a simple query tool. However, with no annotations and no mention of error behavior or permission requirements, an agent might not know how to handle cases like an invalid host_identifier or whether the tool is safe to call without side effects. The output schema exists, so return values are covered, but behavioral context is missing.

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

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

Schema description coverage is 100%: the only parameter, host_identifier, is described as '主机标识（hostid/IP地址/主机名）' (host identifier: hostid/IP/hostname). The description adds the context that this parameter identifies a single host, but it does not add meaning beyond the schema. Baseline 3 is appropriate since the schema already documents the parameter fully.

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

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

The description states a specific verb ('查询' = query) and resource ('单台主机的软件资产信息' = software asset information of a single host), and lists the specific data categories (software/software_full/software_app_a-e). It is clear about what the tool does, though it does not explicitly differentiate from siblings like host_get or item_get, which are broader host-related tools.

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

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

The description implies usage: use this tool when you need software asset information for a single host. It does not explicitly state when not to use it or mention alternatives (e.g., use host_get for general host info, or hostgroup_get_hosts for multiple hosts). The context is clear enough for a single-host query, but no exclusions or alternative routing are provided.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that the tool modifies the name and group, omitting any details about side effects, whether group changes are additive (append) or replacement, permission requirements, or reversibility. The schema hints at append vs. remove via parameter names, but the description itself does not explain these behaviors, leaving significant gaps for an agent.

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

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

The description is a single, concise sentence that efficiently communicates the core action and a key prerequisite. It avoids fluff and front-loads the essential information. However, it is slightly terse, lacking expansion on the group modification semantics, which might be expected for a tool with multiple group-related parameters.

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

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

For a mutation tool with no annotations and four parameters, the description is notably incomplete. It does not explain the append vs. remove behavior of add_groups/remove_groups, whether both can be used together, or any constraints (e.g., cannot remove the last group). The output schema exists but does not cover behavioral context. The prerequisite to query group IDs is mentioned, but broader usage context and operational details are missing, making the description insufficient for confident invocation.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds value by noting that group parameters are group IDs and that they must be queried first, clarifying that these are references to external entities. This goes beyond the schema's simple '要添加的群组ID' and helps the agent understand the nature of these parameters. It does not detail new_name but that is already clear.

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

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

The description clearly states the action ('修改' modify), the resource (主机 host), and the specific attributes modified (可见名称 visible name and 群组 group). It distinguishes the tool from retrieval siblings like host_get or hostgroup_get by focusing on modification. The purpose is unambiguous and specific.

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

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

The description provides a clear prerequisite: '群组参数为groupid（需先查询）' (group parameter is groupid, must query first), implying that the agent should obtain group IDs via a lookup (e.g., hostgroup_get) before calling this tool. However, it does not explicitly mention when to prefer this tool over alternatives or when not to use it, so it lacks explicit exclusions but still offers practical guidance.

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

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

With no annotations provided, the description carries the full burden. It discloses that this is a query operation, requires a hostid from host_get, supports semantic search, and that key_metrics_only intelligently filters to key metrics. It does not discuss auth or rate limits, but the presence of an output schema reduces the need to describe return shape.

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

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

Three short clauses, each earning its place: the purpose, the prerequisite, and the distinctive capabilities. It is front-loaded, scannable, and free of filler.

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

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

Given full schema descriptions, an output schema, and the relative simplicity of a read tool, the description supplies the essential workflow context: the host_get dependency and the semantic-search/key-metrics capabilities. A minor gap is not explicitly noting that itemids can be used as an alternative selector, but the overall definition is sufficient for an agent to invoke the tool correctly in common cases.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds value beyond the schema by connecting hostids to host_get and characterizing key_metrics_only as smart filtering of the best metrics, even though some of this is echoed in the parameter descriptions.

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

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

The description states a specific verb-resource pair — '查询监控项' (query monitoring items) — which clearly distinguishes this tool from siblings like host_get, trigger_get, and event_get. The mention of obtaining hostid via host_get also anchors its role in the broader workflow.

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

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

The description explicitly instructs the agent to first call host_get to obtain hostid, giving a clear precondition for the common host-based use case. It does not explicitly mention alternatives such as querying directly by itemids, but the intended context is reasonably clear.

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

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

With no annotations, the description must carry the behavioral burden, but it only states what it does without disclosing whether it is read-only, what side effects exist, or any rate limits. It does not clarify the exact meaning of 'unify current issue categories' or the response format, leaving behavioral traits largely unspecified.

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

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

The description is a single concise sentence, which is efficient. However, it is somewhat terse and could be structured to highlight the primary purpose more clearly, but it earns a 4 for being appropriately brief.

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

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

Given the tool has an output schema and only two boolean parameters, the description is moderately complete, but it fails to explain the relationship between the parameters and the result, or what 'unify current issue categories' concretely returns. The output schema may compensate, but the description remains vague on the tool's exact behavior.

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

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

Schema description coverage is 100% for both parameters, so the schema already documents include_offline and include_disabled. The description adds no additional meaning beyond the schema, so the baseline score of 3 is appropriate.

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

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

The description states a clear verb (查看) and resource (主机), and adds that it also covers problem classification and recent 24-hour events. It distinguishes itself from sibling tools like host_get or event_get by implying an aggregated quick status view, though the phrase '统一当前问题分类' is somewhat ambiguous.

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

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

No guidance is provided on when to use this tool versus alternatives such as host_get, event_get, or get_problem_summary. There is no mention of prerequisites, exclusions, or specific scenarios where quick_status is preferred.

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

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

No annotations are provided, so the description carries the behavioral burden. It discloses a key behavioral trait—data is hourly aggregated—and also routes summary requests to trend_summary. This is sufficient for a read-only trend fetch, though it does not mention pagination or retention behavior.

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

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

The description is extremely concise: one sentence states the core action and a second sentence points to the summary alternative. Every word adds value and the key information is front-loaded.

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

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

For a four-parameter getter with a fully documented input schema and an output schema, the essential context is covered. It would be slightly better to explicitly mention when to use history_get instead, but the hourly aggregation note and trend_summary routing make the tool's role clear enough.

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

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

Schema description coverage is 100%, so the schema already documents all parameters and their meanings. The description adds no parameter-specific detail beyond what the schema provides, so the baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: retrieve monitoring item trend data, specifically hourly aggregated data. It also differentiates itself by pointing to trend_summary for summary use, making the resource and scope unambiguous.

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

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

It explicitly tells the agent to use trend_summary for summaries, providing a clear alternative. However, it does not explicitly contrast with history_get for raw, non-aggregated data, though the hourly aggregation hint makes the intended use fairly clear.

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

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

With no annotations, the description carries the full burden. It discloses the aggregation logic and output components, but says nothing about permissions, side effects, limits beyond schema, or error behavior. It is minimally adequate but leaves gaps in the safety profile.

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

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

The description is a single information-dense sentence with no filler. It front-loads the primary action and output, making it efficient, though it does not use any structural separation.

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

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

For a 2-parameter tool with an output schema, the description is adequate for basic invocation. However, it lacks any routing guidance relative to trend_get, and since no annotations exist, additional behavioral context would improve completeness.

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

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

Schema description coverage is 100%, so both parameters are already documented. The description adds no parameter-specific meaning beyond the schema; it only describes the operation itself, so the baseline of 3 applies.

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

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

The description states a specific operation: sample-count-weighted aggregation over trend buckets, returning time buckets and sample coverage. This clearly differentiates it from raw retrieval tools like trend_get, though it does not name the sibling explicitly.

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

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

No guidance is provided on when to use this tool versus alternatives such as trend_get or history_get. The description gives no preferred contexts, prerequisites, or exclusions.

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

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

No annotations are provided, so the description carries the burden. It discloses a behavioral trait: priority=3,4,5 only shows severe and above levels. It doesn't mention pagination, default limit behavior, or output format, but the output schema exists and the description adds the priority filtering behavior.

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

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

The description is very short: one sentence plus a priority note. It's front-loaded with the main purpose. The priority note is terse but meaningful. No wasted words.

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

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

For a list-query tool with 10 parameters and an output schema, the description is minimal. It covers the core purpose and one behavioral rule, but doesn't explain parameter interactions, default behavior beyond limit=20, or when to use search vs filter. The output schema exists, so return values are covered elsewhere.

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

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

Schema description coverage is 100%, so the schema already documents all 10 parameters. The description adds the priority filtering rule (priority=3,4,5 only severe and above), which adds meaning beyond the schema's enum list. However, it doesn't explain the relationship between priority and filter/search parameters.

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

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

The description states a clear verb and resource: '查询触发器列表' (query trigger list). It also adds a priority filtering note. However, it doesn't explicitly distinguish itself from sibling tools like item_get or event_get, though the resource 'trigger' is clear.

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

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

The description implies usage for querying triggers and adds a specific priority rule (priority=3,4,5 only severe and above). It doesn't explicitly state when to use this vs alternatives, but the resource-specific name and context make the primary use case clear.

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