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Judge Objective

judge_objective

Queue a background sufficiency judgement for one control objective's mitigation group when it awaits a coverage decision, then re-read the model to see the new state.

Instructions

Have one control objective's mitigation group judged. Mutating — queues background work and may consume credits.

Use this for an objective whose risk_reason is awaiting_judgement: it has a built mitigation group and nothing has decided whether that group covers the objective — never evaluated, evaluated against inputs that have since changed, or the answer parked. The objective is not short of controls, so generating or implementing more will not move it; what is missing is the judgement.

This is not a repair. The judgement can come back insufficient, which moves the objective to coverage_gap / insufficient_by_design and names real work. That is the tool doing its job: it replaces "nobody has looked" with an answer, and the answer may be no.

Scoped to ONE objective, which is the difference that matters against recompute_verdicts: that tool force-enqueues every control's coverage verdict AND every live objective's group-sufficiency verdict, which on a large model runs to thousands of credits. This queues a single judgement. Any credits it consumes are metered at actuals as the work runs, like every other metered call — the account's usage is visible in its billing panel before and after.

Judging runs in the BACKGROUND; the call returns as soon as the work is queued. Re-read get_mitigation_groups (or assess_model / get_risk_view) shortly after to see the objective's new state. Calling again while a judgement is already queued is harmless and does not queue a second one.

A refusal comes back as data rather than an error, so it can be relayed:

{queued: false, http_status: 409, ...} — controls are still being generated for this model. Poll get_control_generation_status until terminal, then call again.
{queued: false, http_status: 503, ...} — judging is unavailable on this deployment.
{queued: false, http_status: 402, code, message} — the balance this workspace bills to cannot cover the judgement.

Input Schema

TableJSON Schema

Name	Required	Description
`co_id`	Yes	ID of the control objective to have judged (e.g. "CO5").
`model_id`	Yes	ID of the threat model.
`server_version`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.83.1Sep 30, 2026

TDQS

A4.6/5.0

Behavior5/5

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

No annotations are provided, so the description carries the full burden and does: mutating, queues background work, consumes credits metered at actuals, returns before work completes, re-calling is harmless/idempotent, and refusal semantics (409/503/402) returned as data rather than errors. This is unusually complete behavioral disclosure.

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?

Front-loads the core action and mutation warning, then structures the when-to-use, the not-a-repair caveat, and refusal codes. Longer than most, but each block (background behavior, refusal codes) earns its place; minor redundancy between the credit note and the metering note.

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?

With an output schema present the description needn't detail return values, and it still covers background execution, how to observe the new state (get_mitigation_groups / assess_model / get_risk_view), and refusal payloads. Nothing an agent needs to invoke it correctly is missing.

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 67% and the description adds little per-parameter meaning beyond the implicit 'one objective' scoping of co_id. It does not clarify server_version (undocumented in schema) or parameter format. Adequate but not compensating for the coverage gap.

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 (judge one control objective's mitigation group) and immediately names the sibling it differs from (recompute_verdicts), plus clarifies what it is NOT ('This is not a repair'). An agent can distinguish it from every sibling without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit trigger: use for an objective whose risk_reason is awaiting_judgement, with the three sub-conditions spelled out. Also gives an explicit exclusion (the objective is not short of controls, so generating/implementing more won't move it) and names the alternative recompute_verdicts with its cost difference.

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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