updateTaskDependency
Change the type (FS/SS/FF) or lag/lead of an existing task-dependency. Doesn't move dates directly; flags the successor with needs_dependency_review=true and fills suggested_start_date/suggested_end_date if the change implies a different schedule.
This record as markdown: /tools/io-stablebaseline-sb/updatetaskdependency.md
What updateTaskDependency does on Stable Baseline
AI agents use updateTaskDependency 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 |
|---|---|---|---|
lagDays | integer | — | Positive = lag, negative = lead. |
dependencyId | string | Yes | |
dependencyType | string | — |
Parameters from the server's own tool schema.
Why updateTaskDependency is rated Medium
This tool modifies task dependency relationships and scheduling suggestions but does not execute irreversible deletions (Destructive), move financial assets (Financial), or trigger external code execution (Execute). The changes are reversible and represent standard data updates to a project management system.
From the tool's definition Tool description states it 'Change[s] the type (FS/SS/FF) or lag/lead of an existing task-dependency' and 'fills `suggested_start_date`/`suggested_end_date`', which are reversible modifications to task scheduling metadata.
Attacks that exploit this kind of access
The rule that runs updateTaskDependency 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 updateTaskDependency, this is the rule to start with:
updateTaskDependency 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 updateTaskDependency call is checked against it from then on.
Questions about updateTaskDependency
Change the type (FS/SS/FF) or lag/lead of an existing task-dependency. Doesn't move dates directly; flags the successor with needs_dependency_review=true and fills suggested_start_date/suggested_end_date if the change implies a different schedule. 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.
updateTaskDependency accepts 3 parameters: lagDays, dependencyId, dependencyType. Required: dependencyId. 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 updateTaskDependency: 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.
updateTaskDependency 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 updateTaskDependency 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 updateTaskDependency. 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.
updateTaskDependency 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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