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SigRank — AI Operator Benchmarking

by com.signalaf
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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
contextYesExplain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): "Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization."
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%.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\"",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "input",
-  "output",
-  "cache_read",
-  "cache_write",
-  "target_percentile"
-]New value: +[
+  "input",
+  "output",
+  "cache_read",
+  "cache_write",
+  "target_percentile",
+  "context"
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds meaningful behavioral context beyond that: it states the tool simulates changes rather than applying them, and it enumerates the return fields including current rank, simulated rank, required pillar changes, and yield delta.

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 compact yet complete, with each sentence serving a distinct purpose: the motivating question, the mechanics, the return values, a high-level positioning statement, and concrete trigger phrases. No sentence is wasted, and the most important behavior is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of an output schema, the description compensates by explicitly listing the returned data points. It covers the target percentile with examples, names the input pillars, and gives clear user-facing trigger questions. Combined with 100% schema coverage and read-only annotations, an agent has enough context to invoke this tool 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 description coverage is 100%, so the schema fully documents all seven parameters. The description adds value by framing input/output/cache_read/cache_write as '4 token pillars' and by giving an example interpretation of target_percentile (90 for top 10%), but it does not substantially go beyond the schema's detailed parameter descriptions.

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 uses a specific question-based framing: 'Answers What would it take to reach a target rank?' and clearly identifies the resource (SigRank metric pillars) and the operation (simulating smallest metric changes). This distinguishes it from simple ranking or leaderboard tools because it emphasizes counterfactual simulation.

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 use-case triggers: 'Use it when someone asks what would move my rank? or how do I get to top 10%?' This is clear contextual guidance. However, it does not explicitly differentiate from the sibling tool simulate_change or say when not to use it, so it stops short of full when-not/alternative coverage.

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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