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SigRank — AI Operator Benchmarking

by com.signalaf

Get Operator — Public Profile by Codename

get_operator
Read-onlyIdempotent

Read one public operator profile by codename. Returns class tier, rank, percentile, Yield, Leverage, Velocity, and SNR.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codenameYesThe operator's unique codename (e.g. signal-ae3b5c3c55). Not the display name.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
snrNoSignal-to-noise ratio.
rankNoGlobal rank position.
yield_NoYield (Υ) if compounding, else null.
codenameNoOperator's unique codename.
leverageNoLeverage if compounding, else null.
velocityNoVelocity = output / input.
class_tierNoOperator class tier.
percentileNoPercentile in the public field.
display_nameNoHuman-readable display name.

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the word 'Read' aligns with them. The description adds useful context that the profile is public and enumerates returned metrics, but it does not disclose error behavior, availability guarantees, or other traits beyond 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.

Conciseness5/5

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

The description is a single, front-loaded sentence that states the operation, scope, and returned data with zero fluff. Every part earns its place.

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 low-complexity single-parameter lookup with rich annotations and an output schema, the description is largely complete. It gives the retrieval key, scope, and result fields; it only lacks a brief note on not-found or invalid-codename behavior.

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%, and the schema already explains that 'codename' is the unique operator codename and not the display name. The tool description adds no additional parameter-level meaning, so the baseline of 3 applies.

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 action ('Read') and resource ('one public operator profile') with a precise lookup key ('by codename'), and it lists the returned fields. It does not explicitly differentiate itself from siblings such as get_sigrank_standard_record, so it stops 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 Guidelines3/5

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

Usage is implied: an agent can infer this tool is for retrieving a single public operator profile when the codename is known. However, the description gives no explicit guidance on when to prefer it over similar sibling tools or when not to use it.

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.

Resources