tasks_update
Update an existing task. Set status='done' to complete it, 'cancelled' to cancel. Use summary for completion notes.
This record as markdown: /tools/io-github-saloprj-dialogbrain/tasks-update.md
What tasks_update does on Dialogbrain
AI agents use tasks_update to create or update resources in Dialogbrain, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Dialogbrain environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
due_at | string | — | ISO datetime |
status | string | — | |
summary | string | — | Completion note (stored when marking done) |
task_id | integer | Yes | ID of the task to update |
agent_id | integer | — | Agent ID whose task to update. Required when calling from MCP. |
priority | string | — | |
description | string | — |
Parameters from the server's own tool schema.
Why tasks_update is rated Medium
This tool modifies task records (status and summary fields) but the changes are reversible—tasks can be updated again, uncompleted, or have notes modified. It does not delete tasks permanently (Destructive), execute arbitrary code (Execute), or involve financial transactions (Financial). It is a standard write operation on task management data, typical of Write category tools.
From the tool's definition Tool description states 'Update an existing task' and allows setting status to 'done' or 'cancelled', plus adding completion notes via summary parameter. These are reversible modifications to task data.
Attacks that exploit this kind of access
The rule that runs tasks_update safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Dialogbrain, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For tasks_update, this is the rule to start with:
tasks_update 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 Dialogbrain, apply this rule, and every tasks_update call is checked against it from then on.
Questions about tasks_update
Update an existing task. Set status='done' to complete it, 'cancelled' to cancel. Use summary for completion notes. It is categorised as a Write tool in the Dialogbrain MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
tasks_update accepts 7 parameters: due_at, status, summary, task_id, agent_id, priority, description. Required: task_id. The full parameter table on this page comes from the server's own tool schema.
Register the Dialogbrain MCP server in PolicyLayer and add a rule for tasks_update: 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 Dialogbrain. Nothing to install.
tasks_update 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 tasks_update 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 tasks_update. 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.
tasks_update is provided by the Dialogbrain MCP server (https://api.dialogbrain.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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