submit_task_feedback

Submit feedback for a completed or in-progress task

SERVERTodo for AI MCP Server SOURCEtodo-for-ai/todo-for-ai-mcp

Medium RISK CLASS

Category Write

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/todo-for-ai-todo-for-ai-mcp/submit-task-feedback.md

WHAT IT DOES

What submit_task_feedback does on Todo for AI MCP Server

AI agents use submit_task_feedback to create or update resources in Todo for AI MCP Server, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Todo for AI MCP Server environment.

RISK

Why submit_task_feedback is rated Medium

This tool creates or modifies task feedback records reversibly. Feedback can typically be updated, deleted, or corrected later, making it a Write operation rather than Destructive. The severity is medium because feedback submission affects task records and could impact project tracking or team coordination if misused by an AI agent, but does not irreversibly delete data or trigger financial transactions.

From the tool's definition Tool name 'submit_task_feedback' and description 'Submit feedback for a completed or in-progress task' indicate modification of task state or metadata through feedback submission.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs submit_task_feedback safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Todo for AI MCP Server, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For submit_task_feedback, this is the rule to start with:

submit_task_feedback Rate-limited

submit_task_feedback 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "submit_task_feedback": {
      "limits": [
        {
          "counter": "submit_task_feedback_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Todo for AI MCP Server, apply this rule, and every submit_task_feedback call is checked against it from then on.

FAQ

Questions about submit_task_feedback

What does the submit_task_feedback tool do? +

Submit feedback for a completed or in-progress task. It is categorised as a Write tool in the Todo for AI MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on submit_task_feedback? +

Register the Todo for AI MCP Server MCP server in PolicyLayer and add a rule for submit_task_feedback: 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 Todo for AI MCP Server. Nothing to install.

What risk level is submit_task_feedback? +

submit_task_feedback is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit submit_task_feedback? +

Yes. Add a rate_limit block to the submit_task_feedback 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.

How do I block submit_task_feedback completely? +

Set action: deny in the PolicyLayer policy for submit_task_feedback. 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.

What MCP server provides submit_task_feedback? +

submit_task_feedback is provided by the Todo for AI MCP Server MCP server (todo-for-ai/todo-for-ai-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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