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SigRank — AI Operator Benchmarking

by com.signalaf

Simulate Change — What-If Cascade Predictor

simulate_change
Read-onlyIdempotent

Prescriptive 'what if' tool — takes your current 4 token pillars and proposed changes, runs the cascade on both, returns the exact Υ Yield delta, class change, and per-metric diffs. Test proposed pillar changes and see the payoff before changing your workflow. Changes can be absolute numbers (replace) or strings starting with +/- for relative deltas.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesCurrent input tokens.
outputYesCurrent output tokens.
changesYesProposed changes. Keys: input, output, cache_read, cache_write. Values are absolute numbers (replace) or strings starting with +/- for relative deltas. Omitted pillars are unchanged.
cache_readYesCurrent cache-read tokens.
cache_writeYesCurrent cache-write tokens.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructveHint=false, covering safety traits. The description adds a useful behavioral detail: it runs the cascade on both current and proposed states and returns deltas, reinforcing that this is a side-effect-free what-if simulation rather than a workflow mutation.

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?

Two sentences, no filler, front-loaded with purpose and outcome. Every sentence earns its place, including the relative-delta clarification.

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?

Despite having no output schema, the description lists the key return values (yield delta, class change, per-metric diffs) and explains the input concept (current pillars + proposed changes). It could be slightly richer with a concrete example, but it is adequate for an agent to invoke correctly.

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 100%, and the schema already documents each parameter, the shape of the changes object, the absolute/relative value distinction, and the 'omitted pillars are unchanged' rule. The description reaffirms this but does not add meaningful param semantics beyond the schema.

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 names a specific verb ('simulate'/'test'), a clear resource ('current 4 token pillars and proposed changes'), and a concrete result ('exact Υ Yield delta, class change, and per-metric diffs'). This sets it apart from diagnostic or improvement-suggestion siblings even without naming them.

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?

The description clearly frames when to use the tool: 'Test proposed pillar changes and see the payoff before changing your workflow.' This is strong usage context, though it does not explicitly mention sibling tools or state when not to use this tool.

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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TDQS

B3.4/5.0
Disambiguation2/5

Several tools have significantly overlapping purposes: benchmark_me and compare_to_field both compare a user's cascade to the field, operator_signature and who_operates_like_me both find comparable operators, and rank_paste/rank_windows overlap as metric calculators. Descriptions clarify the output format, but an agent would frequently struggle to pick the right tool.

Naming Consistency3/5

All names are lowercase snake_case, which is readable, but the pattern is mixed: get_leaderboard and simulate_change are verb-first, field_anomaly and operator_gap are noun-first, and rank_if, rank_paste, benchmark_me, and who_operates_like_me break the convention entirely. There is a loose semantic system, but no strong predictable verb_noun pattern.

Tool Count3/5

16 tools sits right at the heavy borderline, and the count feels inflated by overlapping tools that could be consolidated. The domain is broad enough to justify more than a handful of tools, but the duplication makes the set feel heavier than its actual functional surface.

Completeness4/5

The tool set covers the core benchmarking workflow well: reading leaderboard/operator data, computing metrics, comparing to the field, diagnosing weaknesses, simulating changes, and suggesting improvements. There are minor gaps, such as no way to retrieve a user's raw token pillars from a codename for simulation tools that require four pillars, but these are workable.

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