acceptTaskDependencyReview
Per-item: apply a successor's suggested_start_date/suggested_end_date to its real dates and clear needs_dependency_review. For a whole-plan cascade use applyTaskDependencyCascade.
This record as markdown: /tools/io-stablebaseline-sb/accepttaskdependencyreview.md
What acceptTaskDependencyReview does on Stable Baseline
AI agents use acceptTaskDependencyReview to create or update resources in Stable Baseline, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Stable Baseline environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
improvementId | string | Yes | The successor item whose suggestion to apply. |
Parameters from the server's own tool schema.
Why acceptTaskDependencyReview is rated Medium
The tool modifies task date fields and clears a review flag, which is a reversible data update. It writes new date values and status changes to tasks, but does not delete data or execute code. The blast radius is medium as it can affect project scheduling for individual tasks.
From the tool's definition apply a successor's `suggested_start_date`/`suggested_end_date` to its real dates and clear `needs_dependency_review`
Attacks that exploit this kind of access
The rule that runs acceptTaskDependencyReview safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Stable Baseline, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For acceptTaskDependencyReview, this is the rule to start with:
acceptTaskDependencyReview stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Stable Baseline, apply this rule, and every acceptTaskDependencyReview call is checked against it from then on.
Questions about acceptTaskDependencyReview
Per-item: apply a successor's suggested_start_date/suggested_end_date to its real dates and clear needs_dependency_review. For a whole-plan cascade use applyTaskDependencyCascade. It is categorised as a Write tool in the Stable Baseline MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
acceptTaskDependencyReview accepts 1 parameter: improvementId. Required: improvementId. The full parameter table on this page comes from the server's own tool schema.
Register the Stable Baseline MCP server in PolicyLayer and add a rule for acceptTaskDependencyReview: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Stable Baseline. Nothing to install.
acceptTaskDependencyReview is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the acceptTaskDependencyReview rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for acceptTaskDependencyReview. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
acceptTaskDependencyReview is provided by the Stable Baseline MCP server (https://api.stablebaseline.io/functions/v1/cloud-serve/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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