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review_performance

Analyze code to identify performance bottlenecks, memory leaks, algorithmic complexity (Big O), and async I/O overhead for actionable optimization insights.

Instructions

Identify performance bottlenecks, memory leaks, algorithmic complexity (Big O), and async I/O overhead

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesCode to optimize for performance
modelNoModel to use (default: deepseek-reasoner)
providerNoAI Provider
Behavior3/5

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

Without annotations, the description carries the full burden. It does disclose that the tool performs analysis ('identify') and specifies the categories of issues it detects, which is useful. However, it does not mention whether the tool is read-only, what the output format is, or any side effects, leaving some behavioral ambiguity.

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, well-structured sentence with no unnecessary words. It front-loads the verb and lists all key performance concerns efficiently, making it immediately scannable.

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 tool's simplicity (one required parameter, no output schema), the description provides enough context to understand its purpose and scope. It could be improved by mentioning what kind of output the user can expect, but the description is largely complete for a straightforward analysis tool.

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?

The schema covers 100% of parameters with descriptions, so the baseline is 3. The tool description adds no additional parameter context, but it also doesn't need to since the schema already explains each parameter adequately.

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 uses the specific verb 'Identify' and clearly specifies the resource: performance bottlenecks, memory leaks, algorithmic complexity (Big O), and async I/O overhead. This distinguishes it from sibling tools like review_code, review_security, and review_architecture by focusing on performance-specific concerns.

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 clear context by stating it identifies performance-related issues, making it obvious when to use this tool (e.g., when code performance needs analysis). It does not explicitly mention when not to use it or name alternatives, but the context is clear enough to guide basic selection.

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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