dida365_checkin_habit · Dida365 Agent MCP: Risk & Policy

WHAT IT DOES

What dida365_checkin_habit does on Dida365 Agent

AI agents use dida365_checkin_habit to create or update resources in Dida365 Agent — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Dida365 Agent environment.

WHY IT NEEDS A POLICY

Why dida365_checkin_habit needs a policy

The tool likely updates a habit's state for a given date (e.g., marking it as done today), which is a write operation that modifies data reversibly. This is distinct from deletion (Destructive) or irreversible changes. However, confidence is moderate (0.65) because the description is empty, requiring inference from the name and server context.

From the tool's definition Tool name contains 'checkin' (check-in) and 'habit'; the name suggests marking a habit as completed/checked for a specific day. The server description indicates it manages 'habits through natural language, with full CRUD' operations.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

dida365_checkin_habit Rate-limited

dida365_checkin_habit 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": {
    "dida365_checkin_habit": {
      "limits": [
        {
          "counter": "dida365_checkin_habit_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Dida365 Agent, so you can run agents against it safely.

FAQ

Questions about dida365_checkin_habit

What does the dida365_checkin_habit tool do? +

dida365_checkin_habit. It is categorised as a Write tool in the Dida365 Agent MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on dida365_checkin_habit? +

Register the Dida365 Agent MCP server in PolicyLayer and add a rule for dida365_checkin_habit: 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 Dida365 Agent. Nothing to install.

What risk level is dida365_checkin_habit? +

dida365_checkin_habit is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit dida365_checkin_habit? +

Yes. Add a rate_limit block to the dida365_checkin_habit 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 dida365_checkin_habit completely? +

Set action: deny in the PolicyLayer policy for dida365_checkin_habit. 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 dida365_checkin_habit? +

dida365_checkin_habit is provided by the Dida365 Agent MCP server (linhai0872/dida365-agent). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.