Skip to main content
Glama

SigRank — AI Operator Benchmarking

by com.signalaf
OverviewDiscussionsAdmin

Simulate Change — What-If Cascade Predictor

simulate_change
Read-onlyIdempotent

Prescriptive 'what if' tool — takes your current 4 token pillars and proposed changes, runs the cascade on both, returns the exact Υ Yield delta, class change, and per-metric diffs. Test proposed pillar changes and see the payoff before changing your workflow. Changes can be absolute numbers (replace) or strings starting with +/- for relative deltas.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesCurrent input tokens.
outputYesCurrent output tokens.
changesYesProposed changes. Keys: input, output, cache_read, cache_write. Values are absolute numbers (replace) or strings starting with +/- for relative deltas. Omitted pillars are unchanged.
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.

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",
-  "changes"
-]New value: +[
+  "input",
+  "output",
+  "cache_read",
+  "cache_write",
+  "changes",
+  "context"
+]
  2. First observed

TDQS

A4.2/5.0
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, covering the safety profile. The description adds behavioral value by explaining it 'runs the cascade on both' current and proposed states, returns specific result types, and supports absolute values or '+/-' relative deltas. No contradiction with 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 three sentences with no filler: behavior and outputs are front-loaded, followed by usage intent, then value-syntax guidance. Every sentence earns its place and the structure supports quick agent scanning.

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?

This is a complex tool with 6 required parameters, a nested changes object, and no output schema. The description covers the core outputs (Υ Yield delta, class change, per-metric diffs) and the invocation scenario. It could go deeper on the exact output shape or explicitly mention how the four token pillars map to parameters, but the rich input schema largely carries that burden.

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 already documents all parameter meanings, including the absolute-vs-relative change syntax. The description reinforces this behavior but does not add significant new field-level semantics beyond what the schema provides, so the baseline 3 applies.

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 clearly identifies the tool as a 'what-if' simulator with a specific verb ('simulate'), resource ('4 token pillars... cascade'), and distinctive output (Υ Yield delta, class change, per-metric diffs). This differentiates it from siblings like diagnose_cascade or suggest_improvements.

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 explicitly states when to use it: 'Test proposed pillar changes and see the payoff before changing your workflow.' It gives clear context but does not name sibling alternatives or provide explicit 'use X instead' routing, 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.

All SigRank — AI Operator Benchmarking toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources