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Bulk decide patches

article_review_bulk_decide

Apply a single accept, reject, or reset decision to multiple article patches at once, using filters like status, topic, or patch IDs to update review outcomes in bulk.

Instructions

Accept, reject, or reset all or selected patches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryNo
offsetNo
statusYes
topicIdNo
patchIdsNo
sessionIdYes
contextModeNo
idempotencyKeyYes
expectedVersionYes
Behavior2/5

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

With no annotations provided, the description must carry the full burden of behavioral disclosure. It does not mention the effect of 'reset' (e.g., reverting to pending?), the atomicity of the operation, whether it affects all patches or only those matching criteria, or any side effects like notifications. The description is too terse to understand the behavioral implications of a bulk operation.

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?

The description is a single concise sentence that is front-loaded with the main action. It earns its place by being short, but it is under-specified; however, that is a completeness issue, not conciseness. The structure is appropriate for a simple tool description, though it could be slightly more informative without becoming verbose.

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

Completeness2/5

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

Given the complexity (10 parameters, 4 required, no output schema, no annotations), the description is severely lacking in completeness. An agent would not know which parameters are required (sessionId, status, expectedVersion, idempotencyKey) or their significance. The description does not explain return values, error conditions, or the 'reset' action. The tool is clearly not minimal, and the description leaves the agent to guess.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must explain the parameters. The description mentions 'all or selected patches', which hints at the 'patchIds' and 'status' filtering, but it does not clarify the role of 'status' as an action (e.g., accept/reject) versus a filter, nor does it explain 'expectedVersion' for optimistic locking, 'idempotencyKey' for retries, 'limit/offset' for pagination, or 'contextMode'. The description is insufficient for an agent to correctly construct a request.

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?

The description clearly states the action (accept, reject, or reset) on a specific resource (patches) in bulk, which is distinct from the single-entity tool 'article_review_set_patch_decision'. However, it does not explicitly mention the 'selected' aspect beyond 'all or selected patches' which is somewhat vague, but the core purpose is clear.

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

Usage Guidelines2/5

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

The description implies usage when bulk decisions are needed, but it does not provide explicit guidance on when to use this tool versus the singular 'article_review_set_patch_decision' (e.g., for efficiency with many patches) or when to use one over the other. No alternatives or exclusions are given, leaving the agent to infer the appropriate context.

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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