claude_prompt_structured

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: the tool runs in 'headless, stateless mode', validates output against a schema if provided, returns parsed JSON, and uses the 'server process's current working directory'. This covers execution mode, validation behavior, and environmental context, though it lacks details on error handling 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 appropriately sized and front-loaded, with the core purpose stated first. Every sentence adds value: the first defines the action and output, the second explains schema usage, and the third provides environmental context. There is no wasted text, making it highly efficient.

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 the complexity of a 4-parameter tool with no annotations and no output schema, the description is mostly complete. It covers the tool's purpose, behavioral traits, and usage context. However, it lacks details on error cases, response format beyond 'parsed JSON', or performance considerations, leaving minor gaps for a tool with no structured safety hints.

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 schema already documents all parameters thoroughly. The description adds minimal parameter semantics beyond the schema, only mentioning that the schema is 'used both as an instruction to the model and for lightweight post-hoc validation'. This aligns with the baseline score of 3 when the schema does the heavy lifting.

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's purpose: 'Run a one-shot prompt against the Claude Code CLI in headless, stateless mode and return a structured JSON object.' It specifies the verb ('Run'), resource ('Claude Code CLI'), and distinguishes from siblings by mentioning 'structured JSON object' and schema validation, unlike the generic 'claude_prompt' and context-aware 'claude_prompt_with_context'.

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 provides clear context for when to use this tool: for 'one-shot prompt' execution in 'headless, stateless mode' with structured JSON output. It implies usage for schema-constrained responses but does not explicitly state when NOT to use it or name alternatives like the sibling tools, though the structured output focus differentiates it.

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