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review_code

Review code by having two AI models independently analyze it and then combine their findings into a unified report.

Instructions

Mutual code review on a code snippet. Claude and GPT-4o each review independently, then Claude synthesizes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesCode snippet to review
contextNoOptional context about the code
filenameNoOptional filename for context
languageNoLanguage hint (e.g. python)
synthesizeNoGenerate synthesis report (default true)
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses the multi-model review process and synthesis step, providing useful behavioral context. However, it omits details like return format, side effects, or authorization needs.

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 a single sentence that is front-loaded and contains zero wasted words. Every part conveys essential information about the tool's operation.

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

Completeness3/5

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

With 5 parameters, no output schema, and sibling tools, the description explains the process but lacks usage context and return value information. It is adequate for a simple tool but has gaps in guiding selection among siblings.

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 description coverage is 100%, so baseline is 3. The tool description does not add extra meaning beyond the schema descriptions for parameters like 'code', 'context', 'filename', 'language', and 'synthesize'.

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 clearly states the tool performs a mutual code review on a code snippet, specifying two models (Claude and GPT-4o) review independently then synthesize. It distinguishes from siblings 'review_diff' and 'review_file' by focusing on a code snippet rather than a diff or file.

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?

No explicit guidance on when to use this tool versus alternatives. The description implies it is for standalone code snippets, but does not mention when not to use or provide criteria to differentiate from 'review_diff' or 'review_file'.

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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