todo_complete_task
Marks a Microsoft To Do task as complete (via Reminders sync).
This record as markdown: /tools/com-local-mcp-local-mcp/todo-complete-task.md
What todo_complete_task does on Local
AI agents use todo_complete_task to create or update resources in Local, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Local environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
list | string | — | List name to narrow search by title (optional) |
title | string | — | Task title (partial match, alternative to task_id) |
confirm | boolean | — | Must be true to complete |
task_id | string | — | Task ID from todo_list_tasks |
Parameters from the server's own tool schema.
Why todo_complete_task is rated Medium
This tool performs a reversible modification to task data—changing a task's completion status. This is a Write operation rather than Destructive because the action can be undone (the task can be marked incomplete again).
From the tool's definition The tool 'todo_complete_task' marks a task as complete, which modifies the state of a task in the to-do list. The description states it 'Marks a Microsoft To Do task as complete (via Reminders sync)', indicating a state change operation.
Attacks that exploit this kind of access
The rule that runs todo_complete_task safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Local, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For todo_complete_task, this is the rule to start with:
todo_complete_task 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 Local, apply this rule, and every todo_complete_task call is checked against it from then on.
Questions about todo_complete_task
Marks a Microsoft To Do task as complete (via Reminders sync). It is categorised as a Write tool in the Local MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
todo_complete_task accepts 4 parameters: list, title, confirm, task_id. The full parameter table on this page comes from the server's own tool schema.
Register the Local MCP server in PolicyLayer and add a rule for todo_complete_task: 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 Local. Nothing to install.
todo_complete_task 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 todo_complete_task 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 todo_complete_task. 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.
todo_complete_task is provided by the Local MCP server (local-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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