ml_experiment_update

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

Annotations already indicate readOnlyHint=false and destructiveHint=false, so the mutating-but-not-destructive nature is established. The description adds that only name and description are affected, which is useful, but it does not clarify patch semantics, idempotency, or behavior when only the required IDs are provided.

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 a single, front-loaded sentence with no filler. Every word earns its place, and it directly conveys the tool's core action and scope.

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 simple 4-parameter update tool with no output schema, the description is reasonably complete for identifying the action and target fields. However, it omits guidance about partial updates, the need to provide at least one mutable field, and what happens if invalid IDs are passed, leaving some ambiguity for an agent.

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 baseline is 3. The description loosely maps 'name' to the 'displayName' parameter and 'description' to the 'description' parameter, which adds some clarity, but it does not explain whether at least one optional field is required or whether both can be supplied together.

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 states a specific verb ('Update'), resource ('ML experiment'), and target fields ('name or description'). It clearly separates this from sibling tools like ml_experiment_create, ml_experiment_get, ml_experiment_delete, and ml_experiment_list by indicating a mutation of existing experiment metadata.

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 no explicit guidance on when to use this tool versus alternatives, no prerequisites (e.g., that the experiment must exist), and no mention of when to prefer create/delete/list. Usage is only implicit from the word 'Update'.

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