Compute dependency-based preserve and delete sets for DataLens objects to preview cleanup without executing deletions.
Compute dependency-based preserve and delete sets without deleting anything.
| Name | Required | Description | Default |
|---|---|---|---|
| candidates | Yes | ||
| preserve_roots | Yes |
Changes observed during successful MCP inspections.
v1.2.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is covered. The description adds the 'dependency-based' framing, telling the agent the computation traces dependencies rather than doing a naive set diff, but says nothing about output format or side effects beyond what annotations provide.
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?
A single tight sentence that front-loads the operation and its key constraint. No filler, no 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?
There is no output schema and no parameter documentation, so the description should carry more weight. It never explains the shape of the preserve/delete result or what candidates/preserve_roots should contain, leaving the agent without enough to invoke the tool precisely.
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?
Both required parameters (candidates, preserve_roots) have 0% schema description coverage, and the description does not explain what either represents or how they interact with dependency resolution. With two undocumented required inputs, the description fails to compensate 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?
The description uses a specific verb ('compute') and names the concrete artifacts produced ('dependency-based preserve and delete sets'), which is materially more informative than the tool name. 'Without deleting anything' distinguishes its intent from a cleanup apply operation, though it never names the sibling explicitly.
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?
'Without deleting anything' implies this is the safe preview step before a destructive cleanup, but the description never states when to use it versus dl_cleanup_apply or what to do with the computed sets. Usage is only inferable, not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.