analyze_parallel

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: parallel processing across multiple LLMs for faster analysis, ideal for large codebases. However, it lacks details on error handling, rate limits, authentication needs, or what specific analysis is performed beyond 'analyze'.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by key details and parameter explanations. Every sentence adds value without redundancy, making it efficient and well-structured.

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?

Given the tool's complexity (parallel processing with multiple parameters) and no annotations, the description is fairly complete: it explains purpose, usage context, and parameters. Since an output schema exists, it need not detail return values. However, it could better address behavioral aspects like error handling or limitations.

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 description coverage is 0%, so the description must compensate. It adds meaningful context for all parameters: 'directory' as the path to analyze, 'prompt' as the analysis query, and 'num_workers' as parallel workers with a default. This goes beyond the bare schema, though it could elaborate on parameter constraints or formats.

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 tool's purpose with specific verbs ('analyze', 'distributes') and resources ('large codebase', 'files across multiple LLM services'). It distinguishes from potential siblings by emphasizing parallel processing for speed, which neither 'list_services' nor 'route_task' would imply.

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 provides clear context for when to use this tool ('ideal for analyzing large codebases with 20+ files'), but does not explicitly state when not to use it or mention alternatives. It implies usage for speed and scale without naming specific sibling tools as alternatives.

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