run

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

No annotations were provided, so the description carries the full burden. It covers auto-selection of fastest model with fallback, free_only filtering, min_tier, max_tokens, and temperature. It does not discuss costing or rate limits, but the core behaviors are well 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 somewhat lengthy due to the Arg list, but it is well-structured with clear sections. The main purpose is front-loaded. No unnecessary sentences.

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 8 parameters, 1 required, and an output schema, the description covers all parameters, explains the auto-selection logic, and mentions the response. It is complete for an AI agent to use.

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 description includes an 'Args' section that explains each parameter (prompt, system_prompt, model_id, provider, min_tier, free_only, max_tokens, temperature) beyond the schema titles. With 0% schema description coverage, this is essential and well executed.

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 'Run a prompt on the fastest available model and return the response.' This is a specific verb+resource, and it distinguishes from siblings like batch_run or judge by emphasizing fastest auto-selection.

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 says 'Use when the user wants to execute a prompt.' and explains options for controlling model selection. It implicitly guides when to use alternatives like list_models(). However, it does not explicitly state when not to use this tool.

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