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

Discloses important behavioral traits beyond the sparse annotations: 'the original stays put', 'Rebuilds on the analysis's own dataset by default', and 'Returns pipeline tracking'. This gives the agent a clear picture of mutation, non-destructiveness, and follow-up behavior without needing additional inference.

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 tightly-scoped sentences with no fluff. The core action and required inputs are front-loaded, followed by default behavior, return value, and the recommended follow-up step.

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 a moderate 4-parameter tool with no output schema, the description covers required inputs, default dataset behavior, non-destructive side effects, return semantics, and next step. An agent has enough to invoke and monitor the operation correctly.

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 description coverage is 100%, so the schema already documents all parameters. The description adds value by clarifying that changes are expressed in plain language and that the dataset_ref defaults to the analysis's own dataset, going slightly beyond the schema.

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 action: 'Modify an EXISTING analysis into a new version', with concrete change examples like 'reword the question, swap the method, or add a variable'. This clearly distinguishes it from creation and execution siblings, since it targets existing analyses and produces a new version.

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 clear usage context: pass tool_name + changes in plain language, rebuild on the existing dataset by default, and follow with build_status. It does not explicitly name when-not-to-use alternatives, so it does not fully earn a 5.

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