run_experiment

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

While annotations cover the destructive nature, the description adds critical behavioral traits not found in structured data: the execution is in the background, it is asynchronous (returns when queued), and it provides specific HTTP failure codes.

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 definition is dense but highly efficient; every clause conveys vital operational information regarding execution, failure, and lifecycle management.

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?

The description is highly complete for an asynchronous, stateful operation, covering exactly what the agent needs to know to manage the experiment run, including failure conditions and how to check status.

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 input schema is very simple and fully covered by the schema description (1 parameter), so the description provides minimal additional semantic meaning beyond what is necessary.

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 clearly defines the tool's purpose as starting or restarting an experiment's detection pipeline. It uses specific operational language but does not explicitly differentiate itself from similar sibling tools like create_experiment.

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?

It provides explicit guidance on usage, including identifying specific failure modes (409, 400) and dictating the necessary follow-up actions (polling get_experiment_status or get_experiment_logs).

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