likenessforge_plugins

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

Adds behavior the annotations cannot express: the call returns a retained background job rather than an inline list, so the agent must poll; the base write effect flag is required; and inference/download effects are conditional and must be checked via danceflow_component. This is consistent with readOnlyHint=false and idempotentHint=false and materially changes how an agent should sequence the call.

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?

Four short sentences, front-loaded with what the tool lists, then the async consequence, then effect-flag guidance. Every sentence carries operational information; only the 'Base effect flags' phrasing is slightly terse/opaque.

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?

An output schema exists so return shape needn't be explained, and the description covers the two things an agent would otherwise get wrong: that this is an async job needing polling, and that effects depend on permissions. Minor gap is that it doesn't clarify how installed plugins relate to the models listing 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 50%, with the permissions object carrying its own description ('Acknowledge only effects authorized for this operation'). The description contributes one useful piece of meaning, the base write flag, but says nothing about the network/download/training/inference/publication flags or the empty arguments object, so it only partially compensates for the coverage gap.

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?

States a specific verb and resource: lists installed plugin metadata across four named plugin categories (detector, segmenter, modifier, trainer). That is enough to distinguish it from likenessforge_models (model catalogue) without opening schemas, though the description never explicitly names that sibling contrast.

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?

Gives concrete post-call routing: poll danceflow_job_status for the returned job, and consult danceflow_component for conditional inference/download effects. However it gives no guidance on when to choose this tool versus likenessforge_models or likenessforge_capabilities, so the selection guidance is inferred rather than stated.

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