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Preview a delegate (free)

kimi_delegate_dry_run
Read-only

Preview a delegate call's scope before paying: inspect baseline, prompt size, workspace, and deadline risks without invoking a model. Validate repo and task size to avoid failed or costly delegation.

Instructions

Preview what a kimi_delegate/kimi_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. Free — no model call, no spend, no worktree created.

Use it before delegating to confirm scope and repo before committing to cost, exactly as kimi_dry_run previews kimi_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; the result echoes the effective model/reasoning_effort overrides the paid call would send (unvalidated). deadline_advisory is non-null when size or reasoning effort risks the synchronous deadline and names kimi_delegate_async verbatim — the async counterpart of the previewed call, not of this dry-run tool. A hint, not a refusal.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesThe coding task you want Kimi to implement via a real kimi_delegate call; this dry run only previews the seeded baseline and prompt size — it does NOT call Kimi or return a diff. Must be non-blank: empty or whitespace-only is rejected here too, not previewed.
modelNoThe Kimi model slug the previewed paid call would use; defaults to the server default (MOONBRIDGE_MODEL) when unset, so the preview mirrors the paid call's resolution. This dry run does not call Kimi or validate the model.
isolationNoWhich skills Kimi loads: 'inherit' (own user/project discovery) or 'ignore-skills' (empty dir). Built-ins load either way; this reduces loading, not isolation. Default: server-configured, per kimi_status. More: kimi://params.
workspace_rootNoAbsolute path to the target repo root — pass it to target the intended repo (MCP roots are unavailable); otherwise the call falls back to the server's own cwd and sets meta.workspace_warning.
reasoning_effortNoThe reasoning effort the previewed paid call would send (as a `model_reasoning_effort` config override); defaults to the server default (MOONBRIDGE_REASONING_EFFORT) when unset, so the preview mirrors the paid call's resolution. This dry run does not call Kimi or validate the value beyond the paid params' shape bounds (no control or surrogate characters, ≤128 chars).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
Install Server

TDQS

A4.6/5.0
Behavior5/5

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

The description goes well beyond the readOnlyHint annotation by detailing exactly what happens and what does not: no model call, no spend, no worktree created. It also transparently explains subtle return semantics — tier/sandbox describe the previewed run, overrides are unvalidated, and deadline_advisory is only a hint — which prevents misinterpreting the tool's output.

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?

The description is long, but it is information-dense and front-loaded with the most critical facts (free, no model call, no spend, no worktree). The caveats about tier/sandbox, overrides, and deadline_advisory are valuable and earn their place, though a slightly tighter structure would improve scannability.

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

Completeness5/5

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

For a preview tool with an output schema, the description completely covers the behavioral contract: what is previewed, what is not, validation parity with the paid call, and the meaning of special return fields. It even resolves potential confusion about deadline_advisory naming the async counterpart. Nothing critical is missing.

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?

Input schema description coverage is 100%, so the schema already fully documents every parameter. The description adds useful context about the previewed call's resolutions, but it does not need to compensate for schema gaps. Baseline 3 is appropriate here.

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 identifies a specific action ('Preview what a kimi_delegate/kimi_delegate_async call would do'), names the exact resources involved (HEAD commit, file counts, prompt size, workspace/isolation), and distinguishes this tool from related siblings like kimi_dry_run. The 'exactly as kimi_dry_run previews kimi_review_changes' analogy makes the purpose immediately recognizable.

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

Usage Guidelines5/5

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

The description says explicitly when to use it: 'Use it before delegating to confirm scope and repo before committing to cost.' It also states that a failure here mirrors a failure the paid call would hit, giving the agent a concrete preflight decision rule. It names the paid counterparts (kimi_delegate/kimi_delegate_async) and the analogous dry-run sibling.

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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