This record as markdown: /tools/94aharris-coach-ai/complete-todo.md
What complete_todo does on Coach AI
AI agents use complete_todo to create or update resources in Coach AI, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Coach AI environment.
Why complete_todo is rated Medium
This tool changes data state but does not irreversibly destroy information, does not execute arbitrary code, and does not involve financial transactions. It is a straightforward Write operation that updates a todo's completion status.
From the tool's definition The tool description states it will 'Mark a todo as complete', which modifies the status field of a task record.
Attacks that exploit this kind of access
The rule that runs complete_todo safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Coach AI, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For complete_todo, this is the rule to start with:
complete_todo 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 Coach AI, apply this rule, and every complete_todo call is checked against it from then on.
Questions about complete_todo
Mark a todo as complete. It is categorised as a Write tool in the Coach AI MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Coach AI MCP server in PolicyLayer and add a rule for complete_todo: 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 Coach AI. Nothing to install.
complete_todo 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 complete_todo 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 complete_todo. 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.
complete_todo is provided by the Coach AI MCP server (94aharris/coach-ai). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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