data_transform

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

Annotations already declare the safety profile (readOnlyHint=false, destructiveHint=true, idempotentHint=true), so the description's job is to add value beyond them. It does: it discloses that the tool 'Returns a compact sample and explicit execution effects,' warns that 'large results require output.path,' and explains workspace variable behavior (ADT_WORKSPACE_ROOT). Crucially, the description is consistent with the annotations—the described rewrite/transform behavior aligns with destructiveHint=true, so there's no contradiction.

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 long, but justifiably so given the tool's complexity—a full transformation DSL. It front-loads the purpose, uses a crisp exclusion, and packs workspace semantics and return behavior into tight sentences. The embedded JSON example is bulky but earns its place. It loses one point for the dense JSON blob being potentially hard to scan, but every sentence adds value.

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?

For a tool with 3 top-level params, a huge nested schema (19 step types, multiple assertion types), and no output schema, the description covers the most critical gaps: workspace pathing rules, large-result handling (output.path), the full plan shape, and the validation sibling's boundary. It's slightly shy of a 5 because the 'compact sample and explicit execution effects' return semantics are never detailed, and error/limit behaviors aren't addressed—leaving some burden on the agent despite the otherwise strong guidance.

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?

With 0% schema description coverage, the description must compensate, and it does extensively: it embeds a complete Transformation Plan v1 JSON example showing versions, sources with path/select, steps (filter with condition, select with aliases), and return mode. It also explicitly documents the workspace parameter semantics and the output.path behavior for large results. This is exactly the burden-shifting the rubric requires when the schema provides no narrative.

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 opens with a specific verb phrase — 'Transform or rewrite records: reshape, filter, join, aggregate, cast, flatten, or convert structured data with Transformation Plan v1' — clearly naming the resource (Transformation Plan v1) and listing concrete operations. It differentiates from siblings by explicitly excluding validation tasks ('Do not use this tool for a validation-only request'). This is a strong purpose statement for a tool whose name alone would be ambiguous.

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 gives an explicit when-not-to-use directive with a named alternative: 'Do not use this tool for a validation-only request such as checking non-null or unique fields; use data_validate.' It also provides workspace usage guidance ('Omit workspace with ADT_WORKSPACE_ROOT; otherwise it is an exact granted root name, never a path') and large-result handling. This matches the exemplar behavior in the rubric—clear exclusion plus named alternative.

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