codex_debug

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

With no annotations provided, the description carries full burden. It discloses the multi-turn process ('hypotheses, tests... verdict with evidence chain') and time consumption. While it doesn't detail authentication or rate limits, it adequately describes the behavioral traits for a debugging tool.

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 extremely concise: two sentences that front-load the core purpose and process, then add usage guidance. Every sentence adds value with no wasted words.

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 (10 parameters, multi-turn process, no output schema), the description provides adequate context for an AI agent to decide to use the tool. It explains the multi-turn nature and intended use cases, though it omits details about output format.

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 baseline is 3. The description does not add significant meaning beyond the schema; it restates high-level concepts (symptoms, code, context) but does not elaborate on parameter usage or constraints.

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 it performs 'Structured root-cause analysis via GPT Codex' and specifies usage for 'hard-to-find bugs, unexpected behavior, or production incidents.' This distinguishes it from sibling tools like codex_ask or codex_plan, which have different purposes.

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 explicitly recommends use for debugging scenarios ('hard-to-find bugs, unexpected behavior, or production incidents') and notes time cost ('Can take minutes'). However, it does not explicitly state when not to use it or mention alternatives, though sibling names provide context.

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