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Run BYOK Jev Jury Review

cascade_review_jev

Run up to five Jev by TypeSafe AI jurors on supplied Cascade evidence using the caller key in X-JEV-API-KEY. Requires model_spend_authorized=true. Deterministic vetoes skip provider calls. Returns shadow research review; never submits trades, signs payments, or uses an operator key.

Input Schema

NameRequiredDescriptionDefault
evidenceYes
instrumentYes
observed_atYes
model_spend_authorizedYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior5/5

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

Goes well beyond the annotations: names the required X-JEV-API-KEY header, states that spend must be authorized, explains that deterministic vetoes short-circuit provider calls (cost behavior), and explicitly scopes the output as shadow research that never submits trades, signs payments, or uses an operator key. That is exactly the side-effect disclosure an agent needs for a non-readOnly, open-world tool.

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?

Four tight sentences, each doing distinct work: what it does, auth requirement, cost/veto behavior, and safety scope. Front-loaded with the action and no filler.

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?

No output schema exists, and the description does characterize the return as a shadow research review, plus covers auth, cost, and safety for a mutation-adjacent tool. Missing only detail on the evidence payload format and what a review contains.

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 0%, so the description has to carry the load. It clarifies evidence (Cascade evidence) and model_spend_authorized (spend gate), but says nothing about instrument or observed_at semantics, nor the shape of evidence entries, leaving half the required parameters undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: run Jev jurors (by TypeSafe AI) over Cascade evidence, capped at five. It is clearly distinct from cascade_analyze/capsule/status by naming the jury mechanism, though it doesn't explicitly contrast itself with cascade_analyze.

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?

Gives one hard prerequisite (model_spend_authorized=true) and implies a BYOK key is needed, but never says when to reach for this over cascade_analyze or what evidence qualifies. Usage is implied rather than routed.

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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