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SigRank — AI Operator Benchmarking

by com.signalaf

Rank Windows — Multi-Window Cascade

rank_windows
Read-onlyIdempotent

Score up to 4 time windows (7d, 30d, 90d, all-time) in one call. Each window is scored independently with the full cascade (Υ, SNR, Leverage, Velocity, 10xDEV, class). Omit windows you don't have — partial input is allowed (1-4 windows). Does NOT submit to the board.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
7dNo7-day window token pillars (optional)
30dNo30-day window token pillars (optional)
90dNo90-day window token pillars (optional)
allNoAll-time window token pillars (optional)

TDQS

A4/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds non-redundant behavioral context: each window is scored independently with the full cascade, partial input is accepted, and the tool does not submit to the board. This is valuable beyond what annotations provide, though it omits return-format details.

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, all high-signal: the operation is front-loaded, the cascade and partial-input behavior are explained concisely, and the no-submission boundary is a critical one-liner. Nothing is padded or repetitive.

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?

For a read-only, idempotent scoring tool with four optional nested parameters, the description covers batching, independent scoring, accepted window labels, and side-effect boundary. The lack of an output schema means return-format details are not provided, but the information needed to invoke the tool correctly is present.

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%, with each window property labeled as optional and the inner pillars described as token-related fields. The description reinforces partial-input semantics and the one-call batching, but it does not add meaningful per-field meaning beyond the schema, so baseline 3 is appropriate.

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

Purpose4/5

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

The description clearly states a specific operation ('Score up to 4 time windows'), names the exact windows, and defines boundaries with 'Does NOT submit to the board.' It is unambiguous, but it does not explicitly contrast itself with sibling tools like rank_if or rank_paste, so it falls just short of a 5.

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 gives concrete usage context: one call can score multiple windows, partial input is allowed, and omitted windows are simply not scored. It does not explicitly say 'use this instead of X when...', but the guidance is clear enough for an agent to know when this tool is appropriate.

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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