SigRank — AI Operator Benchmarking

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

Name	Required	Description
`input`	Yes	Current input tokens.
`output`	Yes	Current output tokens.
`changes`	Yes	Proposed changes. Keys: input, output, cache_read, cache_write. Values are absolute numbers (replace) or strings starting with +/- for relative deltas. Omitted pillars are unchanged.
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`cache_read`	Yes	Current cache-read tokens.
`cache_write`	Yes	Current cache-write tokens.
`conversation_id`	No	Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedOct 5, 2026

changedInput schema / properties / context / description

Previous value: -"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.\""New value: +"Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""

addedInput schema / properties / conversation_id

Added value: +{
+  "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.",
+  "type": "string"
+}

addedInput schema / properties / llm_model

Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}

changedInput schema / required

Previous value: -[
-  "input",
-  "output",
-  "cache_read",
-  "cache_write",
-  "changes",
-  "context"
-]New value: +[
+  "input",
+  "output",
+  "cache_read",
+  "cache_write",
+  "changes",
+  "context",
+  "llm_model"
+]

Changed3 schema fields changedSep 3, 2026

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"
+]

First observedSep 1, 2026

TDQS

A4/5.0

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, so the agent knows this is a safe, non-mutating simulation. The description adds useful context about the dual-run comparison (current vs proposed) and the relative/absolute value semantics, but does not discuss auth, rate limits, or the conversation_id lifecycle.

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 tool's identity and value proposition, then the output, then the value-format rule. No filler and nothing repeated.

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 required-heavy (7 required params) simulation tool with nested objects, the description conveys what the tool returns and how changes are encoded. It does not clarify how cache_read/cache_write pillars are treated or whether the conversation_id lifecycle affects parallel calls, but the schema covers those parameters, so the gap is minor.

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 coverage is 100%, so all 8 parameters are already documented in the schema, including the changes object with its +/- relative delta semantics. The description restates the absolute-vs-relative convention but adds no syntax, format, or constraint detail beyond the schema, so 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?

States a specific verb+resource ('simulate change', 'what-if cascade predictor') and the exact output it produces (Υ Yield delta, class change, per-metric diffs). It is clearly distinguishable from siblings like diagnose_cascade (diagnosis vs simulation) and rank_if (ranking vs what-if).

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?

Gives a clear when-to-use frame ('test proposed pillar changes and see the payoff before changing your workflow'), implying the pre-commitment scenario. It does not explicitly name an alternative sibling (e.g., diagnose_cascade) or state when not to use it, 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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Simulate Change — What-If Cascade Predictor

Input Schema

Schema Changelog

TDQS

Glama MCP Gateway

Resources