Returns status and progress of the plan currently being created. This is the primary way to check progress — it returns structured JSON with all progress fields. Poll at reasonable intervals (e.g. every 5 minutes): plan generation typically takes 10-20 minutes (baseline profile) and may take long...
Risk signalsBulk/mass operation — affects multiple targets
Part of the PlanExe server.
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AI agents use plan_status to create or modify resources in PlanExe. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call plan_status repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach PlanExe.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"plan_status": {
"limits": [
{
"counter": "plan_status_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full PlanExe policy for all 11 tools.
These attack patterns abuse exactly the kind of access plan_status gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Returns status and progress of the plan currently being created. This is the primary way to check progress — it returns structured JSON with all progress fields. Poll at reasonable intervals (e.g. every 5 minutes): plan generation typically takes 10-20 minutes (baseline profile) and may take longer on higher-quality profiles. State contract: pending/processing => keep polling; completed => download is ready; failed => terminal error; stopped => user called plan_stop (consider plan_resume). progress_percentage is 0-100 (integer-like float); 100 when completed. Note: steps vary in duration — early steps complete quickly while later steps (review, report generation) take longer. Do not use progress_percentage to estimate time remaining. steps_completed and steps_total give the number of plan generation steps completed and expected (both nullable). current_step is the human-readable label of the most recently completed step (e.g. 'SWOT Analysis'). timing.last_progress_at is an ISO 8601 timestamp of the last progress update (null until the first worker update); use it to compute time-since-last-progress and detect stalls — a gap > 10 minutes with no progress change is a strong stall signal. files lists the most recent 10 intermediate outputs produced so far (files_count gives the total); use their updated_at timestamps as a secondary stall signal. When state is 'failed', the response includes an error dict with failure diagnostics: error.failure_reason (category: generation_error, worker_error, inactivity_timeout, internal_error, version_mismatch), error.failed_step (pipeline step active at failure), error.message (human-readable message), and error.recoverable (true => plan_resume may work, false => use plan_retry). The error dict is absent for non-failed states. Unknown plan_id returns error code PLAN_NOT_FOUND. Troubleshooting: pending for >5 minutes likely means queued but not picked up by a worker. processing with timing.last_progress_at unchanged for >10 minutes likely means stalled — call plan_stop then plan_retry. Fall back to file updated_at timestamps if last_progress_at is null. Report these issues to https://github.com/PlanExeOrg/PlanExe/issues .. It is categorised as a Write tool in the PlanExe MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the PlanExe MCP server in PolicyLayer and add a rule for plan_status: 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 PlanExe. Nothing to install.
plan_status 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 plan_status 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 plan_status. 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.
plan_status is provided by the PlanExe MCP server (https://mcp.planexe.org/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 11 PlanExe tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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