audit_performance_bottlenecks

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does list what kinds of performance traps it checks, which adds specific value beyond the generic name. However, it does not state whether the operation is read-only, whether it has side effects, what the output format will be, or any prerequisites. For an analysis tool, these are important but not fully disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, efficient and front-loaded with the core action ('Analyzes code for performance traps'). The list of specific examples adds value without excessive verbosity. No redundant phrases or filler. While the list is somewhat detailed, it remains concise and informative.

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

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

The description explains the tool's core function and scope, listing several concrete performance issues it identifies. However, since there is no output schema, the description does not explain what the tool returns (e.g., a list of findings, severity scores, or suggestions). It also omits any constraints on input size or format beyond the parameter name. These gaps are notable but not critical for a simple analysis tool.

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

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

The input schema already provides a complete description of the 'code_snippet' parameter ('Code snippet or query to audit for performance bottlenecks'), covering 100% of parameter semantics. The tool description adds minimal extra meaning—it mentions 'code' and 'SQL queries' but essentially reaffirms what the schema already states. Thus, baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Analyzes' and the resource 'code for performance traps', with specific examples (React re-render cascades, unindexed SQL queries, N+1 problems, heavy bundle imports, missing image optimization). This distinctly separates it from sibling audit tools like audit_security_vulnerabilities or audit_ui_design_aesthetic, making its purpose unambiguous.

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

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

The description implies that this tool is for performance bottleneck analysis, but it does not explicitly state when to use it versus other audit tools or provide any exclusions. No mention of alternative tools or when not to use it. The usage is inferred from the tool name and description, not explicitly guided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.