AI agents use log_entry to create or update resources in Jhabit — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Jhabit environment.
This tool creates a new entry (log record) associated with a tracker. It is a data creation operation that is reversible (entries can be deleted via the sibling delete_entry tool), placing it in the Write category. Misuse could pollute habit tracking history but is recoverable, warranting medium severity.
From the tool's definition "Log an entry for a tracker" — creates a new log/entry record for a habit tracker
Attacks that exploit this kind of access
Log an entry for a tracker. For habits this means. It is categorised as a Write tool in the Jhabit MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Jhabit MCP server in PolicyLayer and add a rule for log_entry: 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 Jhabit. Nothing to install.
log_entry 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 log_entry 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 log_entry. 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.
log_entry is provided by the Jhabit MCP server (jacob-stokes/jhabit-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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