analyze_code_ast

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

No annotations provided, so description carries full burden. It details how AST works (filters comments/strings, tracks variables, identifies functions, verifies semantic intent) and lists detections. Lacks mention of potential side effects or limits, but sufficient.

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?

Well-structured with a lead sentence, bullet points for benefits and detections, and an Args list. Every sentence adds value, though slightly lengthy; could be more concise but not wasteful.

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?

Output schema exists, so return values not needed. Description covers all parameters, use cases, and behavior. Complete for a complex 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?

Schema coverage is 0%, but description includes an Args section explaining each parameter's purpose (code, file_path, language, deep_analysis), adding meaning beyond schema titles and types.

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?

Clearly states 'Analyze code using AST for GDPR compliance' with specific languages (Python, JavaScript, TypeScript). Distinct from siblings like 'analyze_application_code' which likely uses different methods.

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?

Explains when to use AST over regex (higher accuracy, reduces false positives, tracks data flow) and lists specific detections. Does not explicitly mention when not to use or compare to direct siblings, but context is sufficient.

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