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codex_delegate_dry_run

Read-onlyIdempotent

Preview what a delegate call would do without executing it. Check baseline commit, file counts, prompt size, and workspace to verify scope before spending.

Instructions

Preview what a codex_delegate/codex_delegate_async call would do — the baseline it seeds from (HEAD commit, tracked file count/size, uncommitted and untracked counts), the prompt size that would be sent, and the resolved workspace/isolation — with NO model call, NO spend, and no worktree created.

Use it before delegating to confirm scope and repo before committing to cost, exactly as codex_dry_run previews codex_review_changes. Mirrors the real delegate's zero-spend validation (workspace, isolation, task size, git repo), so a failure here is a failure the paid call would also hit. The returned tier/sandbox describe the previewed propose run, not this read-only preview.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesThe coding task you want Codex to implement via a real codex_delegate call; this dry run only previews the seeded baseline and prompt size — it does NOT call Codex or return a diff.
modelNoThe Codex model slug the real codex_delegate call would use; this dry run does not call Codex or validate the model.
isolationNoCodex config isolation: 'inherit' (default), 'ignore-config', or 'ignore-rules'.
workspace_rootNoAbsolute path to the target repository root. Pass it (or rely on an MCP root) so the call targets the intended repo; otherwise it falls back to the server's own cwd and meta.workspace_warning is set.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
Behavior5/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds layers: no model call, no spend, no worktree creation, and that failure here mirrors real delegate failure. No contradictions.

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

Conciseness4/5

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

Four sentences, with efficient structure. Some redundancy but overall concise and front-loaded with the core function.

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

Completeness4/5

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

Given 4 params, output schema exists, and sibling tools, the description covers tool purpose, usage, and outcome. It explains returned tier/sandbox briefly, which is sufficient.

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 coverage is 100%, so baseline is 3. The description does not add per-parameter details but provides context like workspace_root fallback and isolation purpose. No additional parameter semantics needed.

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

Purpose5/5

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

The description states the tool previews a codex_delegate call with no model call, no spend, no worktree. It distinguishes itself from siblings by comparing to codex_dry_run for codex_review_changes.

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

Usage Guidelines4/5

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

It says to use the tool before delegating to confirm scope and repo, and that failure here predicts failure in the paid call. No explicit when-not, but clear context.

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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