run_ml_project

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

The annotations only indicate that the tool is not read-only, idempotent, or destructive; the description adds no additional behavioral context. It does not disclose whether running a project is asynchronous, whether it creates a job, or what side effects it may have on the project.

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 or repetition. It is concise, though slightly too terse to provide useful operational context.

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?

Although an output schema exists, the description omits important context such as whether the run is asynchronous, whether it submits a job, and how it relates to get_job_status or cancel_job. For a tool that likely triggers a pipeline, this is incomplete.

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?

The schema fully documents the single parameter project_id as 'ID of the project to run,' so the description adds no extra meaning beyond the schema. This meets the baseline for high schema coverage.

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 ('Run') and resource ('AutoML pipeline automation project'), which distinguishes it from create_ml_project and list_ml_projects. However, it does not clarify what 'run' entails, such as whether it starts an asynchronous job or executes synchronously.

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?

There is no guidance on when to use this tool versus alternatives like submit_batch_job, execute_sas_code, or create_ml_project. It also does not mention prerequisites or follow-up actions such as checking job status.

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