SigRank — AI Operator Benchmarking

Operator Gap — What Separates Two Operators

operator_gap

Read-onlyIdempotent

Answers 'What specifically separates operator A from operator B?' — not just 'A has more Yield', but the primary cause, secondary cause, and offsetting weakness. Takes two codenames or two sets of pillars, computes both cascades, and decomposes the yield gap into leverage, velocity, SNR, and scale contributions. Returns the most explanatory factor.

Input Schema

TableJSON Schema

Name	Required	Description
`a_input`	No
`b_input`	No
`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."
`a_output`	No
`b_output`	No
`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.
`a_codename`	No	Codename for operator A (alternative to a_* pillars)
`b_codename`	No	Codename for operator B (alternative to b_* pillars)
`a_cache_read`	No
`b_cache_read`	No
`a_cache_write`	No
`b_cache_write`	No
`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: -[
-  "context"
-]New value: +[
+  "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"
+}

addedInput schema / required
```
Added value: +[
+  "context"
+]
```

First observedSep 1, 2026

TDQS

A3.6/5.0

Behavior3/5

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

Annotations already establish readOnly, idempotent, non-destructive behavior, so the safety profile is covered. The description usefully adds that both cascades are computed and a single most-explanatory factor is returned, but says nothing about determinism guarantees, cost, or handling of partial inputs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

Led by the core question and then progressively elaborates cause hierarchy and the decomposition, so it is front-loaded and dense. Slightly long with em-dash clauses, but every clause adds distinct information.

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

Completeness3/5

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

For a 13-parameter, no-output-schema analytics tool, the description covers the conceptual input modes and the returned factor but leaves the codename-vs-pillar precedence, required context/llm_model plumbing, and conversation_id sequencing to the schema. Adequate but with clear gaps.

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 only 38% across 13 parameters, so the description must carry weight. It explains the two input modes ('two codenames or two sets of pillars'), which covers a_codename/b_codename and the a_*/b_* pillar params at a high level, but adds no meaning for context, llm_model, conversation_id, or the distinction between read/write/input/output pillars.

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 question it answers ('What specifically separates operator A from operator B?') and enumerates the decomposition (leverage, velocity, SNR, scale) plus the output (the most explanatory factor). This clearly distinguishes it from siblings like compare_to_field or get_operator, which don't decompose a gap.

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?

The description implies when to call it by framing the comparison question, and notes it accepts 'two codenames or two sets of pillars'. However it never names alternatives (e.g., compare_to_field or who_operates_like_me) or states when NOT to use it, leaving routing to inference.

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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Operator Gap — What Separates Two Operators

Input Schema

Schema Changelog

TDQS

Glama MCP Gateway

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