sample_tool

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

No annotations are provided, so description carries full burden; it discloses that the tool returns a task_id (async behavior) and has a special requirement for SAS-CPU simulator, but lacks details on destructiveness, auth, or rate limits.

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 concise (3 sentences), front-loaded with purpose, and every sentence adds value with no redundancy.

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?

Given 6 parameters and an output schema, the description only partially covers the parameters and behavior; it explains return flow but lacks detail on parameter defaults and constraints.

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?

With 0% schema description coverage, the description adds meaning for 'qasm' (OpenQASM 2.0 string) and 'amplitude_index' (required for SAS-CPU simulator), but does not cover other parameters like 'shots', 'transpile', 'backend_name', or 'optimization_level'.

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 states the tool submits a sampling task, specifies the required input format (OpenQASM 2.0 string), and distinguishes it from sibling tools like estimate_tool or list_backends_tool by focusing on execution.

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 mentions polling with get_task_status_tool and get_task_result_tool, implying usage flow, but does not explicitly state when to use this tool over alternatives or provide when-not scenarios.

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