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SigRank — AI Operator Benchmarking

by com.signalaf

Field Anomaly — Unusual Patterns in the Leaderboard

field_anomaly
Read-onlyIdempotent

Finds unusual operators, metric relationships, and outliers in the live leaderboard — without user prompting. Returns: highest velocity among below-median leverage operators, only top-50 operator with near-zero cache write, largest 30-day yield improvement, rarest signature, and extreme divergence. Powers automated micro-marketing and field insights.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNo30d

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already cover readOnlyHint, idempotentHint, and destructiveHint. The description adds meaningful behavior: it is proactive ('without user prompting'), operates on the 'live leaderboard', and enumerates concrete return values. There is no contradiction with the annotations.

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

Conciseness4/5

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

The description is compact and front-loaded with the main action. The return list is long but earns its place because there is no output schema. No filler or redundant restatement of the tool name/title.

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 the simple one-parameter schema, strong annotations, and absence of an output schema, the description is largely complete: it explains purpose, automation context, and representative outputs. The main gap is not stating how the `window` parameter affects the returned anomaly set.

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 0%, and the description never mentions the `window` parameter or explains that it controls the anomaly lookback period. However, the only parameter is an optional enum with self-describing values ('7d', '30d', '90d', 'all_time') and a default, so the schema itself carries most of the meaning.

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 a specific verb ('Finds') and a specific resource ('unusual operators, metric relationships, and outliers in the live leaderboard'). It also distinguishes the tool from siblings by framing it as an automated anomaly-detection tool for micro-marketing/field insights, rather than a general leaderboard query or ranking tool.

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 states this runs 'without user prompting' and that it powers automated micro-marketing and field insights, giving an agent a clear sense of when to select it. It does not explicitly name sibling alternatives or list when-not-to-use conditions, so it stops short of a 5.

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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