run_ai

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

Annotations (readOnlyHint=false, destructiveHint=false) leave ambiguity, but the description adds key behavioral traits: the confirm gate and preview-only mode. This clarifies execution vs. preview behavior. It does not disclose side effects like costs or auth beyond the gate, but informs about the critical mutation-limiting mechanism.

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?

Two concise sentences with no wasted words. Front-loaded with the core purpose and immediately follows with critical usage nuance. Efficient and scannable.

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 no output schema and rich parameter descriptions, the description covers the essential usage (what it does, when to use confirm). It could mention that outputs vary by model, but schema input descriptions hint at this. Overall adequately complete for a single inference 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 100% with detailed descriptions for each parameter. The description adds phrasing like 'text generation, embeddings, classification' summarizing model capabilities, but this is largely implicit in the model parameter description. The confirm gate behavior is already in schema. Thus baseline 3.

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 runs inference on Workers AI models, listing specific capabilities (text generation, embeddings, classification). It distinguishes this tool from siblings (none other do inference) by specifying the core action and resource.

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 explicitly explains the human-approval gate (confirm:true) and that without it returns a preview. This guides when and how to use the tool effectively. It does not mention when not to use or alternatives, but given no similar sibling tools, this is acceptable.

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