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

With no annotations, the description carries the full behavioral burden and states the key side effect (interrupted command rc=1) and the post-condition (engine remains alive and usable). It does not disclose what happens when no command is running, 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.

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

A single, well-structured sentence that front-loads the action, then states the condition and consequences. 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?

The tool is simple with one optional parameter and no output schema, and the description covers the core operation. However, it does not mention the return behavior of the tool or the no-running-command case, leaving those details undocumented.

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% and the schema already documents session_id's meaning and default ('default'). The description only repeats '指定会话' without adding extra semantic value, matching the baseline of 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 uses a specific verb ('打断'/'interrupt') and a concrete resource ('指定会话正在运行的 Stata 命令'), which clearly identifies the tool's action. It is easily distinguishable from sibling tools like stata_run or stata_get_results.

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 phrase '若有' indicates the tool should be used when a Stata command is currently running in a session, giving clear contextual guidance. It does not mention alternatives or exclusions, but the use case is unambiguous.

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

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

No annotations are provided, so the description carries the full behavioral burden. It correctly implies a read-only retrieval and specifies the output shape, but it does not disclose what happens when no dataset is loaded, whether session state is affected, or how errors are surfaced.

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 compact sentence that front-loads the action, target resource, and return format. Every part contributes useful information, with no filler or redundant repetition of the tool name.

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

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

For a simple read-only row-fetching tool, the description covers the essential purpose and return format, while the schema covers parameters. It does not mention prerequisites such as having a dataset loaded or error behavior, but these are minor for a tool of this simplicity.

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

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

The input schema documents both parameters fully, including constraints, defaults, and meaning (rows 1-50 default 10, session_id defaults to 'default'). The description adds no additional parameter semantics, so the schema-based baseline 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?

Description states a specific verb (返回) and resource (当前数据集前 N 行), and even clarifies the return format as a structured two-dimensional array. It is clear about what the tool does, though it does not explicitly differentiate it from sibling tools like stata_inspect_data.

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 phrase '供 agent 直接查看数据' implies this is the tool to use when the agent needs to view the dataset rows directly. However, there is no explicit guidance about when to choose this tool over alternatives, nor any exclusions or prerequisites.

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

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

没有提供注释，描述应承担完整的行为披露负担。它只说明了导出并返回路径和大小，但未提及文件覆盖行为、错误条件（如无图时）、默认文件名生成或会话的作用。这些缺失的信息对安全调用该工具很重要。

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?

一句话包含核心操作和返回值信息，没有冗余。信息前置，简洁高效。

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?

在缺少注释和输出模式的情况下，描述提供了基本功能但未覆盖关键行为细节（如默认文件名、覆盖、错误处理）。对于只有4个可选参数的工具来说不算严重缺陷，但可以让调用者更好地准备依赖默认行为。

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?

输入模式覆盖率为100%，所有参数在模式中都有描述，包括枚举、默认值和说明。描述本身没有添加额外的参数语义，因此根据基线规则打3分。

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

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

描述明确说明了动作（导出）、资源（内存中的图）和结果（图片文件，返回路径和大小）。这使它有别于运行或获取结果的兄弟工具。唯一小的不足是“图”没有进一步说明，但总体上足够清晰。

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

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

描述未提供任何何时使用、何时不用或替代方案的指导。没有提及前置条件（如必须存在图）或与兄弟工具的比较，只能依赖工具名称和语义推断用途。

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?

There are no annotations, so the description carries the full burden. It discloses the source (.sthlp) and output format (plain text), which is useful. However, it does not describe failure behavior, session dependencies, or whether any state is changed. For a read-only help lookup this is a mild gap, not a contradiction.

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 entire description is a single, front-loaded sentence with no filler. It conveys the core function, the output format, and the practical rationale in just a few 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 simple two-parameter tool with no output schema, the description plus fully documented schema provide enough for an agent to call it correctly. It lacks edge-case information like unknown topics or session requirements, but overall it is adequately complete for the tool's simplicity.

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 both 'topic' and 'session_id' adequately. The description adds no parameter-specific detail 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 clearly states a specific verb ('查' / query) and resource ('Stata 官方帮助'), and adds the output format ('纯文本，从 .sthlp 读'). It does not explicitly name or contrast with sibling tools, so it stops short of full sibling differentiation.

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

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

The phrase 'agent 不必瞎猜命令语法' gives a clear context for when to use this tool: before running a Stata command when syntax is uncertain. It does not explicitly state when not to use it or name alternative tools, but the usage context is clear.

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

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

With no annotations, the description carries the disclosure burden. It explicitly states the tool is read-only and draws from session memory, which covers the main safety trait. However, it does not mention what happens when no results exist, whether a session must first be active, or that results may be overwritten by later commands.

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 dense sentence that front-loads the purpose, the data source, and the read-only nature. Every phrase earns its place with no repetition or 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?

For a simple, optional-parameter read-only retrieval tool, the description gives enough information about what is returned and where it comes from. It does not fully explain the result structure, but the absence of an output schema and the low complexity keep this from being a major gap.

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% and the single session_id parameter is already well documented in the schema. The description adds no parameter-specific meaning, but none is really needed; baseline 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 uses a specific verb ('返回') and names a distinct resource: in-memory e()/r()/shape structured results in the current session. This clearly separates it from tools like stata_get_help and stata_session_history, though it does not name a 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?

The phrase '当前会话内存' implies it should be used after running Stata commands that produce e() or r() results, but it does not explicitly state when to use it versus alternatives or include exclusions. The usage context is inferable but not fully spelled out.

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 present, so the description must carry the burden. It discloses output behavior: cleaned output, and structured results for summarize/describe. The word '查看' (view) implies read-only inspection, but the description does not explicitly state non-modification, prerequisites like loaded data, or how session_id affects 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?

Two short sentences with no filler. The core purpose is front-loaded ('查看当前数据'), followed by the available actions and output characteristics. Every phrase contributes useful information.

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 3-parameter tool with no output schema, the description plus a complete schema covers the purpose, parameters, and return behavior adequately. Minor ambiguities remain around '清洗输出' and the exact meaning of 'summarize（单变量）', but they do not prevent correct invocation.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds little beyond the schema; '可限定变量名列表' mirrors the variables parameter, and the action enum is fully documented in the schema with clear per-option explanations.

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 lets you 'inspect current data' via describe/summarize/codebook with an optional variable list. The output note about cleaned and structured results helps distinguish it from a generic command runner like stata_run, though it does not explicitly name sibling 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 opening phrase '查看当前数据' gives a clear usage context: use this when you need to inspect the current dataset. The three enumerated actions define its scope. However, it does not mention when not to use it or suggest alternatives like stata_run for arbitrary commands.

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 disclosing behavior. It does mention the return value (cleaned output and {source, N, k}), and the act of 'loading into memory' implies a session state change. Yet it does not explicitly disclose the risk of overwriting unsaved data or that the operation mutates the Stata session; this side-effect detail is only present in the schema's clear parameter, not in the description itself.

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, compact piece of prose that front-loads the primary action and resource, then states the return shape. Every sentence contributes useful information with no filler, repetition, or unnecessary caveats.

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

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

For a tool with three parameters, no output schema, and no annotations, the description covers the core invocation details: what data sources are allowed, that it returns structured output, and what that output looks like. It does not explain session_id semantics, but the schema covers that, and the absence of an output schema is compensated by the explicit return shape. The missing explicit mention of memory-replacement side effects is a minor gap.

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 no parameter-level meaning beyond what the schema already provides; it summarizes the domain but does not clarify any parameter details beyond the schema's own descriptions. This is acceptable because the schema covers all parameters thoroughly.

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 ('载入' / load), a clear resource ('数据集到 Stata 内存'), and enumerates supported formats and sources (.dta/.csv/.xlsx/.xls, local or https URL). It also mentions the return shape, making the tool's purpose unmistakable and easily distinguishable from sibling tools like stata_inspect_data or stata_run.

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

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

The description gives clear context about what this tool is for and places constraints on where files must live (working directory or authorized directories), which helps an agent decide if it can use it. However, it does not explicitly state when to prefer this over alternatives or when not to use it, leaving the selection logic partially implicit.

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 and reveals substantial behavior: session persistence across calls, cleaned output production, background execution returning job_id immediately, restricted mode intercepting shell escapes (shell/winexec/!), erase, and unauthorized paths, and session isolation via independent Stata instances. Error/exception behavior and the exact result shape are not disclosed, which keeps it from a 5.

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 main description is a single front-loaded sentence with zero filler, stating action, key behavior, and return value. The four parameter descriptions are thorough but each sentence earns its place by adding behavioral semantics, defaults, recommendations, or sibling routing.

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 moderately complex execution tool with no annotations and no output schema, the definition covers purpose, session-state semantics, async workflow, security interception, and session isolation. The main gap is the lack of a precise return-format/error-behavior description (what the cleaned output object looks like, what happens on a non-zero rc), which matters more because there is no output schema.

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

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

Schema description coverage is 100% with rich, behavior-oriented descriptions: code explains multi-line loops and shared memory state, background names the polling/interrupt siblings and suggests async for long commands, restricted details the security config fallback, and session_id explains isolation with independent Stata instances. The tool description adds the rc return concept, complementing the schema. This exceeds the baseline 3 for high coverage.

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

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

The description uses a specific verb and resource — '执行一段 Stata 代码' (execute a block of Stata code) — and states the distinguishing traits: it maintains session state across calls and returns cleaned output text plus rc. This clearly differentiates it from the sibling tools (stata_break, stata_task_status, stata_get_help, etc.), which are all secondary or query operations.

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 main description implies this is the primary execution tool, and the background parameter gives clear operational guidance: use true for long commands (bootstrap/mixed models) and route to stata_task_status/stata_break for polling and interruption. There is no explicit 'when not to use' statement, but the context for choosing sync vs. async is clear.

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

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

With no annotations, the description carries the behavioral burden. It explicitly characterizes this as returning a log with sequence/command/return-code, and the crash-replay mention implies persistence. It does not spell out that it is side-effect-free, but '返回' plus the log/replay framing make the read-only nature evident.

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 sentence conveys action, resource, content, and purpose without filler. Every element 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?

For a simple read-only history tool, the description plus fully documented schema covers the essential invocation details: what is returned, when to use it, and the optional parameters. A brief note on output ordering or total size would make it fully complete, but nothing critical is missing.

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

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

Schema coverage is 100%, and both parameters (last, session_id) have meaningful descriptions in the schema. The tool description adds no extra meaning about these parameters, so the baseline 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 names a specific verb ('返回') and resource ('当前会话的命令日志') and specifies the returned fields (seq/cmd/rc). It does not explicitly contrast with sibling tools like stata_get_results or stata_run, so it misses the strongest form of differentiation.

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

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

The phrase '供追溯或崩溃后重放' states clear use cases (tracing and crash replay), which tells an agent when this log-retrieval tool is appropriate. It does not name alternatives or state when not to use it, but the intended context is clear.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does this well by specifying the three possible return states (running, done with result text, error) and the failure behavior for an unknown job_id (rc=1). This gives the agent concrete expectations beyond just 'query status.' It does not cover every edge case, but the disclosed behavior is meaningful and non-obvious.

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 compact sentence that front-loads the core purpose, lists all relevant status outcomes, and includes the key error condition. Every element earns its place, and there is no redundant 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?

For a simple one-parameter status-check tool with no output schema, the description is notably complete: it covers the states, the result-text behavior, and the error behavior. Combined with the schema's explanation of job_id's origin, an agent has enough information to invoke the tool correctly and interpret likely outcomes.

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 itself does not add much parameter-level meaning, but the schema already describes job_id as a global id returned by stata_run(background=True). The description and schema together fully clarify the single parameter's meaning.

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 a specific resource ('后台任务' / background task), and enumerates the possible statuses: running, done (with result text), and error. It clearly identifies the tool's purpose, though it does not explicitly contrast itself with sibling tools like stata_get_results.

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 implicitly indicates when to use the tool: when checking the status of a background task. The parameter schema adds the key context that job_id comes from stata_run(background=True) and is global, which helps the agent choose this tool for post-run status checks. However, there is no explicit 'use this instead of...' guidance or exclusions relative to siblings such as stata_get_results.

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