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review

Detect issues, analyze patterns, identify refactoring opportunities, and find duplicates in PySpark code.

Instructions

Review PySpark code for issues, patterns, and refactoring opportunities.

Modes:

code Review PySpark code for issues, best practices, and performance. Parameters: code (required), focus_areas

patterns Analyze code samples to discover common patterns. Parameters: code_samples (required list)

duplicates Detect duplicate patterns across code samples. Parameters: code_samples (required list)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeNo
modeYes
focus_areasNo
code_samplesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations exist, so the description must carry the full burden. It describes the modes but does not explicitly state side effects, permissions, or that it is read-only. Given the output schema exists, some behavioral context is implied but not explicit.

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 concise and well-structured with clear headers and backticks for modes. Every sentence adds value, and the main purpose is front-loaded.

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?

Given the output schema exists, return values need not be explained. The description covers the three modes and parameter dependencies. It could mention limitations or prerequisites, but overall it is complete for the tool's complexity.

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?

Schema description coverage is 0%, so the description compensates by explaining the role of each parameter per mode (e.g., 'code' required in code mode, 'code_samples' in patterns/duplicates). This adds significant meaning beyond the raw schema.

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 tool reviews PySpark code for issues, patterns, and refactoring opportunities. It differentiates three modes, but does not explicitly distinguish it from the sibling tool 'analyze', which could be seen as similar.

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

Usage Guidelines4/5

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

The description provides explicit modes and the parameters required for each, guiding when to use each mode. However, it does not specify when not to use this tool or mention alternative sibling tools.

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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