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Get Verdict Divergence

get_verdict_divergence

Surface confident mismatches between AI verdicts and the threat model, listing missing mappings to accept and spurious mappings to remove so coverage reflects the model's true state.

Instructions

Where the LLM's verdicts disagree with the model's authored state.

Two coverage divergence kinds, distinguished by the LLM's p_covers (probability the control covers the CO), shown as "model confidence":

missing_mapping: HIGH p_covers, but the CO is NOT mapped — the LLM is confident the control covers it, so it should be mapped. Accepting ADDS the mapping.
spurious_mapping: LOW p_covers, but the CO IS mapped — the LLM is confident the control does NOT cover it, so the mapping is likely wrong and inflates apparent coverage. Accepting REMOVES the mapping. Only confident rows surface; the uncertain middle band is dropped. So a ~100%-confidence row is a strong "add" and a ~0%-confidence row is a strong "remove" — both are actionable, in opposite directions.

Rows are sorted by confidence, so the strongest calls come first. Each section is paginated: its pagination.filtered_total reports the full count, so when it exceeds the rows returned, raise limit (up to 500) or page with offset to review every divergence — not only the first page.

Also returns group_sufficiency divergences (observation-only). Apply coverage rows with accept_coverage_divergences; set aside rows the structural model got right with dismiss_verdict_divergences.

Input Schema

TableJSON Schema

Name	Required	Description
`kind`	No	Optional filter — "missing_mapping", "spurious_mapping", or "group_sufficiency". Empty returns all kinds.
`limit`	No	Max rows per section (clamped to 1-500, default 100). Set to 500 to pull an entire section in one call.
`offset`	No	Skip the first N rows of each section, for pagination.
`model_id`	Yes	ID of the threat model.
`server_version`	Yes
`include_dismissed`	No	When true, return ONLY previously-dismissed rows (the undo view) instead of the active list.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.66.0Aug 5, 2026

TDQS

A4.8/5.0

Behavior5/5

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

With no annotations provided, the description fully carries the behavioral disclosure burden, and it does so richly. It explains that the uncertain middle band is dropped, rows are sorted by confidence, pagination includes filtered_total, and group_sufficiency divergences are observation-only. This gives the agent important behavior beyond the raw schema.

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 dense but every sentence earns its place: definition, divergence kinds, confidence interpretation, pagination behavior, and follow-up tool routing. It is front-loaded with the core concept and then progressively adds operational details without redundancy.

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?

For a tool with no annotations and moderate complexity, the description is remarkably complete. It covers what the tool returns, how to interpret confidence, how pagination works, and how to act on the results. The existence of an output schema means the exact return structure does not need to be spelled out in the description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is high (83%), but the description goes beyond it by explaining the meaning of kind values (missing_mapping vs spurious_mapping) and how they map to add/remove actions. It also clarifies the semantics of limit and offset in the pagination context, which adds value beyond the schema 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 states exactly what the tool does: returns cases where the LLM's verdicts disagree with the model's authored state. It then details the two coverage divergence kinds (missing_mapping and spurious_mapping) plus group_sufficiency, giving the agent a precise idea of the resource and its semantics. This is a specific verb+resource definition that clearly separates it from generic 'get' tools.

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 strong context on when and how to use the tool: it says only confident rows surface, sections are paginated, and it tells the agent to follow up with accept_coverage_divergences or dismiss_verdict_divergences. It stops short of explicitly stating when not to use it relative to other 'get' tools like get_reachability_verdicts, so it is clear but lacks an explicit exclusion.

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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