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

Goes well beyond the annotations: discloses ordering (newest first up to limit), the live keepAdding behavior with sampling ratios and the 1,000 cap, the effect of each purpose mode on item contents, and the needs_confirmation return path. Annotations (readOnlyHint=false, destructiveHint=false) match the additive create semantics.

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?

Dense but front-loaded: the core action leads, then purpose semantics, then live-mode behavior and next steps. Every sentence carries information, though the paragraph is long and could be broken into clearer chunks.

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 12-parameter mutation with no output schema, it covers the confirmation flow, ordering, caps, and follow-on actions needed to invoke and continue correctly. Nothing an agent would need before calling is missing.

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%, so the baseline is 3, but the description adds real meaning: it translates the terse 'review | eval | spec' enum into what each item keeps, and elaborates keepAdding as every-one or 1-in-10/1-in-100. It leaves workspace and approvalId handling to 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 verb and resource ('Saves the conversations... into a new dataset or an existing one') and pins the scope by reference to caliper_traces_list's same narrowing (day, tool, document, failures). An agent can tell it apart from caliper_datasets_create or caliper_datasets_add_items without opening any schema.

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 clear selection logic between newDatasetName and datasetId, explains when to set keepAdding to make it live, and routes the agent to the next step (caliper_evals_create). It does not explicitly compare against sibling routing like caliper_datasets_add_items, so it stops short of full when-not guidance.

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