القائمة
OpenClaw skill for connecting to PlanExe via Model Context Protocol. Supports three deployment scenarios: cloud-hosted service, remote Docker, and local Docker.
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استنادا إلى تصنيف SOC المهني
Use when the user wants to extract parameters from a PlanExe extraction-input digest (the markdown produced by experiments/napkin_math/prepare_extract_input.py — the 137-recommended section bundle, with the four "Keep or compress" sections compressed) instead of the full PlanExe HTML report
Use when the user wants to extract parameters, modelling values, or key variables from a PlanExe report (HTML or text) for napkin math, triage, or Monte Carlo simulation
Use when the user wants to generate low/base/high assumption ranges (bounds) for missing or uncertain variables in a validated extract-parameters-from-full JSON, in preparation for deterministic scenarios or Monte Carlo
Use when the user wants to run the napkin-math pipeline end-to-end on a PlanExe report, or resume a partially populated output directory by filling in only the missing stages. Orchestrates digest preparation, parameter extraction, validation, bounds, calculations, scenarios, Monte Carlo, and assessment rendering. Never copies artifacts forward from prior runs, and never re-runs a stage whose output is already on disk.
Use after the napkin_math pipeline has produced parameters/bounds/scenarios/montecarlo JSON to generate a plan assessment (assessment.md) — a thin interpretation layer over the intermediary artifacts. Emits a JSON manifest, a provenance map, gate verdicts (Critical / Fragile / Marginal / Robust), failure drivers, confidence and trust boundaries, scenario sanity check, and suggested next actions. The artifact is a navigation/judgment file, not a copy of the raw simulation data.
Use after the napkin_math pipeline has produced parameters.json (from extract-parameters-from-digest or extract-parameters-from-full) to validate it against the 16 structural checks the rest of the pipeline assumes. Writes validation.json next to parameters.json. Deterministic Python — no LLM call.
| name | planexe-mcp |
| description | OpenClaw skill for connecting to PlanExe via Model Context Protocol. Supports three deployment scenarios: cloud-hosted service, remote Docker, and local Docker. |
| version | 1.0.0 |
| author | PlanExe Team |
Access PlanExe's powerful planning capabilities directly from OpenClaw via the Model Context Protocol (MCP).
The planexe-mcp skill provides a unified interface to interact with PlanExe across three deployment scenarios:
mcp.planexe.org (Stripe credits at home.planexe.org)Connect to the hosted PlanExe MCP service.
Configuration:
Add to your OpenClaw config or environment:
PLANEXE_MCP_URL=https://mcp.planexe.org
PLANEXE_API_KEY=your_api_key_from_planexe_account
How to Get Your API Key:
Run PlanExe in Docker on a separate machine and connect remotely.
On the Remote Machine:
docker run -p 8001:8001 planexe/planexe:latest
Configuration in OpenClaw:
PLANEXE_MCP_URL=http://<remote-ip>:8001
PLANEXE_API_KEY=your_api_key
Replace <remote-ip> with your remote machine's IP address or hostname.
Run PlanExe in Docker on the same machine as OpenClaw.
Setup:
docker run -p 8001:8001 planexe/planexe:latest
Configuration in OpenClaw:
PLANEXE_MCP_URL=http://127.0.0.1:8001
PLANEXE_API_KEY=your_api_key
The PlanExe MCP exposes nine core tools via the /mcp endpoint:
example_plansPreview curated example plans with download links for reports and zip bundles.
No parameters required:
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "example_plans",
"arguments": {}
}
}
Returns: List of example plans with title, report_url, and zip_url.
example_promptsGet example prompts to understand what PlanExe can do.
No parameters required:
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "example_prompts",
"arguments": {}
}
}
Returns: List of example prompts for planning tasks.
plan_createCreate a new planning task. This is the main entry point for generating plans.
Parameters:
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "plan_create",
"arguments": {
"prompt": "Create a project launch plan for Q2 2026",
"model_profile": "premium",
"start_date": "2026-04-01T20:55:00+02:00",
"user_api_key": "your_optional_api_key"
}
}
}
Parameter Guide:
prompt (required): Your planning request in natural language. Write as flowing prose (not structured markdown), typically 300-800 words.model_profile (optional): One of "baseline", "premium", "frontier", or "custom". Defaults to "baseline".start_date (optional): Plan start date in ISO 8601 format with timezone offset (e.g. "2026-04-01T20:55:00+02:00"). When omitted, the plan starts now.user_api_key (optional): Your PlanExe API key (if not set in environment)Returns: task_id for polling status and retrieving results.
plan_statusPoll the status of a running planning task.
Parameters:
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "plan_status",
"arguments": {
"task_id": "task_abc123def456"
}
}
}
Usage: Planning tasks typically take 10-20 minutes (baseline profile). Poll every 5+ minutes to check progress.
Returns: Current status (pending, processing, completed, failed), and progress percentage.
plan_stopStop a running planning task.
Parameters:
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "plan_stop",
"arguments": {
"task_id": "task_abc123def456"
}
}
}
Returns: Confirmation that the task has been stopped.
model_profilesReturn available model profiles and their guidance before calling plan_create.
No required parameters:
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "model_profiles",
"arguments": {}
}
}
Returns: Available model profiles (baseline, premium, frontier, custom) with descriptions and currently loaded models.
plan_file_infoRetrieve download information for completed plan artifacts.
Parameters:
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "plan_file_info",
"arguments": {
"task_id": "task_abc123def456",
"artifact": "report"
}
}
}
Artifact Options:
"report": Interactive HTML report (~700KB, self-contained — open in a browser)"zip": Pipeline output bundle (md, json, csv intermediary files)Returns: download_url for accessing the artifact.
plan_listList recent tasks for an authenticated user. Useful for recovering a lost task_id.
Parameters:
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "plan_list",
"arguments": {
"user_api_key": "pex_your_key_here",
"limit": 10
}
}
}
Returns: List of recent tasks with task_id, state, progress_percentage, created_at, and prompt_excerpt.
plan_retryRetry a failed task with an optional upgraded model profile.
Parameters:
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "plan_retry",
"arguments": {
"task_id": "task_abc123def456",
"model_profile": "premium"
}
}
}
Returns: Confirmation that the task was requeued to pending state.
example_plans to preview what PlanExe output looks likeexample_prompts to understand available planning scenariosmodel_profiles to choose an appropriate model_profileplan_create with your prompt and parameters → receives task_idplan_status every 5+ minutes until status is completed or failedfailed, optionally call plan_retry to requeue with a stronger modelplan_file_info with completed task_id to get download linktask_id, call plan_list with your user_api_key to recover itRefer to the PlanExe API documentation for extended examples and advanced use cases.
| Variable | Example | Description |
|---|---|---|
PLANEXE_MCP_URL | https://mcp.planexe.org | MCP endpoint URL |
PLANEXE_API_KEY | pk_live_abc123... | API authentication key |
Connection Refused
PLANEXE_MCP_URL is correctdocker ps)Authentication Failed
PLANEXE_API_KEY is validTimeout Errors
Docker Issues (Local/Remote)
docker --versiondocker logs <container-id>lsof -i :8001 (macOS/Linux)For issues or questions:
This skill is part of PlanExe and is available under the same license as the PlanExe project.