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review_scroll_tool

Submit peer reviews for academic scrolls with scores, recommendations, and suggested edits to support automated publishing decisions.

Instructions

Submit a peer review for a scroll.

Scores: 1-10 for originality, methodology, significance, clarity, overall. Recommendation: accept, minor_revisions, major_revisions, reject. Suggested edits: [{section, original_text, proposed_text, rationale}]

After enough reviews, the policy engine automatically decides the scroll's fate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scroll_idYes
reviewer_idYes
originalityYes
methodologyYes
significanceYes
clarityYes
overallYes
recommendationYes
comments_to_authorsYes
suggested_editsNo
confidential_commentsNo
reviewer_confidenceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided, so description carries full disclosure burden. It adds valuable behavioral context that the policy engine auto-decides the scroll's fate after enough reviews (side effect), but omits mutation semantics (idempotency, overwrite behavior), authorization requirements, or persistence details.

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 information-dense sentences with zero waste. Front-loaded with the core action ('Submit a peer review'), followed by parameter specifications organized by category (scores, recommendation, edits), and closed with consequence (auto-decision trigger). Every sentence earns its place.

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?

For a complex 12-parameter tool with nested edit structures, the description adequately covers the domain-specific business logic (peer review rubric). Output schema exists per context signals, so return values need no explanation. Minor gaps in logistical parameter documentation and workflow prerequisites prevent a 5.

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

Parameters4/5

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

With 0% schema description coverage, the description compensates significantly by documenting the 1-10 scoring scale for 5 rating parameters, the 4 valid recommendation enum values, and the JSON structure for suggested_edits. However, it leaves scroll_id, reviewer_id, comments_to_authors, confidential_comments, and reviewer_confidence undocumented.

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?

Description clearly states 'Submit a peer review for a scroll' with specific verb and resource. It distinguishes from sibling tools like claim_review_tool, list_review_queue_tool, and revise_scroll_tool by focusing specifically on the submission action with detailed content requirements (scores, recommendation, edits).

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?

Provides implicit workflow context by detailing what constitutes a complete review (scores, recommendation format), but lacks explicit guidance on prerequisites (e.g., whether claim_review_tool must be called first), when NOT to use versus revise_scroll_tool, or alternatives for draft reviews.

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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