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batch_results

Read-onlyIdempotent

Retrieve batch simulation job status and progress, or get per-signal aggregate statistics and per-run data from sweep and Monte Carlo runs.

Instructions

Query a batch simulation job (sweep or Monte Carlo). Without signal: returns job status and progress. With signal: returns aggregate statistics or per-run data for that signal.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesBatch job ID from run_sweep or run_montecarlo
signalNoSignal name for per-signal stats (e.g., 'V(out)')
filtersNoFilter runs by parameter values (e.g., {'R1': '10k'}). Applies in both aggregate and raw mode (requires signal).
atNoOptional time (transient) or frequency (AC) point in SPICE notation (e.g., '1k', '100u'). When given, each run is sliced to a single sample at that point before aggregating. Without this, the per-run peak across the full waveform is used, which conflates startup/roll-off with run-to-run variation on AC sweeps.
offsetNoPagination offset for raw data
limitNoMax raw data rows to return (server caps at 50; page with offset)
rawNoReturn per-run raw data instead of aggregate stats
formatNoResponse format: 'json' for structured data, 'text' for human-readable

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idNo
job_typeNo
statusNo
netlistNo
total_runsNo
completed_runsNo
failed_runsNo
modeNo
signalNo
run_countNo
statsNo
max_case_runNo
min_case_runNo
runsNo
paginationNo
convergence_warningsNoPer-run convergence-fallback markers (Gmin stepping, source stepping, etc.) detected in the per-run logs. Present only when at least one run hit a fallback.
Behavior5/5

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

Annotations already declare readOnly=true, destructive=false, idempotent=true. The description adds valuable behavioral details: mode switching based on signal, pagination via offset/limit, and the effect of the 'at' parameter on data slicing. No contradictions.

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 two concise sentences, front-loaded with main purpose. Every word is informative; no redundancy or filler.

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

Completeness5/5

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

Given 8 parameters (1 required), existing output schema, and the tool's complexity, the description covers essential behavior: two modes, pagination, and parameter interactions. It is sufficiently complete for an agent to select and invoke correctly.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds crucial context beyond the schema. For example, it explains how the 'at' parameter changes aggregation behavior and warns about waveform peak conflation without it. This extra guidance significantly aids parameter usage.

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

Purpose5/5

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

The description clearly states the tool queries a batch simulation job (sweep or Monte Carlo). It specifies two modes: without signal returns job status/progress, with signal returns aggregate or per-run data. This specificity distinguishes it from sibling tools like check_job and run_sweep.

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

Usage Guidelines3/5

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

The description explains when to use the signal parameter vs not, but does not mention when not to use this tool or explicitly list alternatives (e.g., check_job for job status). Usage context is implied but lacks 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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