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codex_execute

Execute OpenAI Codex tasks synchronously with full control over subcommand, arguments, model selection, and reasoning effort, returning only core results to save context.

Instructions

Execute OpenAI Codex (GPT-5) synchronously with full control over subcommand and arguments. Returns only the core result, filtering out thinking process to save context. Common usage: subcommand="exec", prompt="your task", args=["--full-auto"]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
argsNoAdditional command-line arguments. Model selection: ["-m", "gpt-5-codex"] for coding (default) or ["-m", "gpt-5"] for analysis. Reasoning effort: ["--config", "model_reasoning_effort=low|medium|high"] (gpt-5-codex supports low/medium/high; gpt-5 supports minimal/low/medium/high). Example: ["--full-auto", "-m", "gpt-5", "--config", "model_reasoning_effort=high"]. Always include "--full-auto" for non-interactive execution.
promptNoMain prompt/argument for the command (required for exec, optional for others)
timeoutNoTimeout in seconds (default: no limit)
subcommandNoCodex subcommand to executeexec
Behavior3/5

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

No annotations present, so the description carries the full burden. It discloses that execution is synchronous and that the thinking process is filtered out, but does not address error handling, timeouts, authentication needs, or potential side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loading the core purpose and key behavioral trait (synchronous, filters thinking). No redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of an output schema, the description should clarify the return format beyond 'core result.' It also lacks guidance on error conditions. However, the example usage helps contextualize parameter use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the schema already provides detailed parameter documentation. The description adds a common usage example but does not significantly extend meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Execute OpenAI Codex (GPT-5) synchronously' with specific verb and resource. It mentions synchronization, which hints at differentiation from the async sibling, but does not explicitly contrast them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a common usage example and mentions subcommands, but does not explicitly state when to use this tool versus the async sibling or other alternatives. No when-not-to-use guidance is given.

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

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