ml_experiment_list

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered and the description is consistent with it. The description adds that the operation returns all experiments scoped to a workspace, but does not disclose behavior such as pagination, result limits, or ordering. With annotations handling safety, this is adequate but not rich.

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, front-loaded sentence with zero filler. The verb, resource, and scope appear in the first six words, and there is no redundant restatement of the tool name or schema fields.

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 one-parameter list tool with read-only annotations and high schema coverage, the definition is nearly complete. An agent can call it correctly with the information provided. The only minor absence is behavioral detail like pagination or return format, which is low-stakes for a scoped read-only enumeration.

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% — workspaceId is already documented as 'The workspace ID'. The description reinforces the linkage by mentioning 'in a workspace', which maps directly to the parameter, but adds no syntax, format, or constraint details beyond the schema. Baseline 3 applies.

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 ('List'), a distinct resource ('ML experiments'), and a clear scope ('in a workspace'). This cleanly distinguishes it from siblings such as ml_experiment_get (single-item retrieval), ml_model_list (different resource type), and the ml_experiment_create/update/delete mutation tools.

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 phrase 'in a workspace' implies the tool is used to enumerate experiments within a workspace context, and the read-only nature is clear from the verb. However, the description does not explicitly state when to prefer this over alternatives like ml_experiment_get, nor does it name exclusions. Usage context is implied rather than stated.

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