ask_perplexity_for_instructions

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

No annotations are provided, so the description carries full responsibility. It mentions that Sonar models have internet access and can search, but does not disclose potential side effects, authentication requirements, rate limits, or what happens on failure. The behavioral disclosure is minimal.

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 structured with a header, optimization note, examples, and model info. Each section serves a purpose, though it could be slightly more concise. Overall, it is well-organized and front-loaded.

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 and the presence of an output schema, the description covers basic purpose and examples. However, it lacks details on instruction format, limitations, or additional context needed for safe use. It is adequate but not fully comprehensive.

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%, yet the description only lists parameter names and a model list without explaining differences or providing validation guidance. It adds minimal value beyond the bare schema, failing to compensate for the lack of schema descriptions.

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 sends a prompt to Perplexity and returns step-by-step instructions executable by the AI agent. It distinguishes itself from siblings like 'ask_perplexity' and 'ask_perplexity_exact_response' by emphasizing multi-step tasks and executable format.

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 explicit examples of good prompts (e.g., 'How do I set up a Python FastAPI project with Docker?') and states the tool is optimized for multi-step tasks. It does not explicitly exclude scenarios or name alternatives, but the context makes usage clear.

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