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gokeshenzhen

io.github.gokeshenzhen/traceweave

by gokeshenzhen

period

Estimate a signal's dominant period within a waveform window and flag the first beat that deviates, helping identify clock stalls, dropped burst beats, and backpressure bubbles.

Instructions

Estimate a signal's dominant period inside a window and flag the first beat that deviates from it. Use for rhythm/throughput questions an LLM cannot eyeball from a transition dump: stalled clocks, dropped burst beats, backpressure bubbles, irregular strobes. The dominant period is the median edge-to-edge interval; the first off-beat is auto-registered as a cursor. Reads existing waveforms only — does NOT rerun simulation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
edgeNoEdge to count. 'any' for multi-bit/strobe signals. Default posedge.posedge
signalYesFull hierarchical signal path.
wave_pathYesWaveform (FSDB or VCD).
cursor_nameNoOptional explicit cursor name for the first off-beat. Defaults to beat_<sha8>.
cursor_noteNoOptional note attached to the registered cursor.
end_time_psNoWindow end. -1 means end of simulation. Accepts an integer (ps), a cursor reference like '@div_3a7c', or a unit literal like '12.34ns'.
start_time_psNoWindow start. Default 0. Accepts an integer (ps), a cursor reference like '@div_3a7c', or a unit literal like '12.34ns'.
tolerance_fracNoFraction of the period a beat may deviate before counting as an off-beat. Default 0.05 (5%).
Behavior5/5

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

With no annotations provided, the description carries the full burden, and it does so well. It discloses that the tool only reads existing waveforms and does NOT rerun simulation, and it reveals the important side effect that the first off-beat is auto-registered as a cursor. It also defines the dominant period as the median edge-to-edge interval, clarifying the internal behavior.

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

Conciseness5/5

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

Three sentences with no wasted words: the first states the core action, the second gives concrete use cases, and the third explains the algorithm, side effect, and read-only safety. The key differentiators are front-loaded.

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

Completeness4/5

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

Given eight parameters and no output schema, the description covers the important behavioral context: when to use it, how the period is computed, the cursor side effect, and the read-only guarantee. The only gap is that the return value is not explicitly described, which matters because no output schema exists.

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 description coverage is 100%, so the input schema already documents every parameter in detail. The description adds some context through the 'window' and 'beat' language, but it does not add meaning beyond the schema, 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.

Purpose5/5

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

The description uses a specific verb ('Estimate', 'flag') and names the resource ('a signal's dominant period inside a window'), then adds the exact calculation method (median edge-to-edge interval). It distinguishes itself from rhythm/throughput alternatives by explicitly scoping to cases an LLM cannot eyeball from a transition dump.

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

Usage Guidelines4/5

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

It clearly states when to use the tool: for rhythm/throughput questions such as stalled clocks, dropped burst beats, backpressure bubbles, and irregular strobes. It also conveys that this is for cases an LLM cannot eyeball, which implies a rough when-not boundary, though it does not name specific alternative sibling tools or explicit exclusions.

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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