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SigRank — AI Operator Benchmarking

by com.signalaf

Rank If — Counterfactual Rank Simulator

rank_if
Read-onlyIdempotent

Answers 'What would it take to reach a target rank?' — takes your current 4 token pillars and a target percentile (e.g. 90 for top 10%), then simulates the smallest metric changes needed to reach that position. Returns: current rank/percentile, simulated rank/percentile, the specific pillar changes required, and the yield delta. This turns SigRank from a scoreboard into a simulator. Use it when someone asks 'what would move my rank?' or 'how do I get to top 10%?'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesCurrent input tokens.
outputYesCurrent output tokens.
windowNoTime window for field comparison (default 30d).30d
cache_readYesCurrent cache-read tokens.
cache_writeYesCurrent cache-write tokens.
target_percentileYesTarget percentile (0-100). E.g. 90 for top 10%, 99 for top 1%.

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly=true, idempotent=true, and destructive=false, so safety is covered. The description meaningfully adds behavioral detail by explaining it simulates the smallest metric changes and returns a yield delta, going beyond basic read-only semantics.

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, front-loaded with the core question the tool answers, then inputs, outputs, and use cases. No filler or redundant restatement of schema details.

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?

With no output schema, the description wisely enumerates the returned data: current rank/percentile, simulated rank/percentile, pillar changes, and yield delta. It is complete enough for an agent to call the tool, though a note about the optional window parameter or differentiation from simulate_change would make it fully robust.

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 parameters are already fully documented. The description adds the useful grouping of '4 token pillars' and clarifies target_percentle semantics with examples, but does not need to compensate for schema gaps.

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 answers a clear question, names the exact inputs (4 token pillars, target percentile), and states what is returned. It positions the tool as a simulator rather than a scoreboard, though it doesn't explicitly differentiate it from sibling simulate_change.

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 explicit trigger scenarios: 'what would move my rank?' and 'how do I get to top 10%?'. It offers clear usage context but does not state when not to use it or mention alternative sibling tools.

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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