blockrun_image

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

No annotations are provided, so the description carries the burden. It discloses payment via USDC, pricing tiers, and model capabilities. However, it omits error states, output format, or rate limits. It is adequate but could be more transparent.

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 well-structured with clear sections: overview, actions, models for generate and edit. It is concise (about 15 lines) and front-loaded with the primary purpose. Every sentence adds value without redundancy.

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 is comprehensive for a complex tool with 7 parameters and two actions, covering pricing and model selection. However, it lacks information about return values or error handling (no output schema). This missing detail reduces completeness.

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 86%, and the description adds significant value beyond the schema. For example, it explains model strengths (e.g., 'nano-banana-pro for 4K photorealism') and gives model-specific size recommendations. This helps the agent select appropriate parameters.

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: 'Generate or edit images via BlockRun.' It distinguishes between generate and edit actions, and lists numerous models with specific capabilities. This sets it apart from sibling tools like blockrun_video or blockrun_music.

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?

Provides explicit guidance on when to use generate vs edit, and gives model recommendations (e.g., which model for text, photorealism, cheap drafts). However, it does not explicitly state when to avoid this tool or suggest alternatives beyond the sibling list.

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