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sw_generate

Generate wireless channel datasets with configurable parameters, returning a handle and statistical summary for validating communication ideas without writing simulations from scratch.

Instructions

生成信道数据集,返回句柄与统计摘要(不返回数据本身)。

两种用法:

  • 协商过:只传 draft_id

  • 用户说"随便,默认就行":直接传 intent,跳过协商

返回里的 auto_decided 列出了替用户做的决定,请转述给用户, 这样他事后想改也知道改什么。

prereg_id 是 sw_lock_analysis 返回的预注册句柄。传了它,主指标与基线会 随数据存档,之后 sw_compare_results 能判断用的指标是不是事先定的那个。 只能在生成前绑定——事后补绑没有意义。

workers 默认 "auto":按配置预估耗时决定要不要起多进程。多小区大带宽的 配置能快 3 倍以上;轻配置起进程反而更慢,会自动走串行。 并行时各块用不同 seed,结果与串行统计等价但逐样本不同(摘要里会写明)。

collect_ssb=False 关掉每小区 SSB RSRP/SINR 的计算,多小区场景省约 30% (交错重测中位数 3456 -> 2475 ms/样本,基准自身轮间波动 11.9%)。 代价是 Dataset.ssb 为空——小区选择、切换、波束管理类课题需要它,别乱关。 默认 None = 保留,不静默减少数据。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentNo
presetNo
workersNoauto
draft_idNo
overridesNo
prereg_idNo
collect_ssbNo
num_samplesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations provided, the description carries the full disclosure burden. It reveals that the tool does not return data, that auto_decided lists user decisions to relay, that workers may run parallel with different seeds, and quantifies the collect_ssb performance tradeoff. This is far beyond a minimal statement.

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

Conciseness5/5

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

The description is long but every section serves a purpose: usage modes, prereg binding, workers behavior, and collect_ssb tradeoff. It is structured with clear headings and bolded warnings, making it scannable despite the detail.

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

Completeness4/5

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

The description thoroughly covers core behavior, performance implications, and user-facing output, and the output schema exists to explain return values. However, it omits descriptions for three parameters (preset, overrides, num_samples) and does not state what happens if neither draft_id nor intent is provided. This leaves minor gaps for a tool of this complexity.

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

Parameters3/5

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

Schema coverage is 0%, so the description must compensate. It meaningfully explains draft_id, intent, prereg_id, workers, and collect_ssb, but leaves preset, overrides, and num_samples completely unexplained. It adds significant value for most parameters but is incomplete for the full parameter set.

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

Purpose5/5

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

The description opens with '生成信道数据集,返回句柄与统计摘要(不返回数据本身)', clearly stating the action (generate), the resource (channel dataset), and the return type (handle + stats, not data). This distinguishes it from sibling tools like sw_describe_dataset or sw_validate.

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

Usage Guidelines5/5

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

Explicitly explains two usage modes ('协商过:只传 draft_id' and '用户说"随便,默认就行":直接传 intent'), when prereg_id must be bound ('只能在生成前绑定'), and when to keep collect_ssb enabled ('小区选择、切换、波束管理类课题需要它,别乱关'). This provides clear when-to-use and when-not-to-use guidance.

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

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