planexe-mcp
Skill402 starsChanged 3 months agoMITView on GitHub
OpenClaw skill for connecting to PlanExe via Model Context Protocol. Supports three deployment scenarios: cloud-hosted service, remote Docker, and local Docker.
- Reads credentials
We split this skill into the rules it gives an agent
20 rules. About 0 are common, 7 are distinctive, and 13 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈7
Distinctive · estimate
This author's own choices. Worth a look.
13
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 28
Add to your OpenClaw config or environment:
- Very specific: line 46
Run PlanExe in Docker on a separate machine and connect remotely.
- Very specific: line 67
Run PlanExe in Docker on the same machine as OpenClaw.
- Distinctive: line 132
Create a new planning task.
- Distinctive: line 189
Stop a running planning task.
- Very specific: line 310
Call example_prompts to understand available planning scenarios
- Very specific: line 314
Call plan_create with your prompt and parameters → receives task_id
- Very specific: line 316
If failed, optionally call plan_retry to requeue with a stronger model
- Very specific: line 317
Call plan_file_info with completed task_id to get download link
- Very specific: line 319
If you lose a task_id, call plan_list with your user_api_key to recover it
- Very specific: line 333
Verify PLANEXE_MCP_URL is correct
- Distinctive: line 335
Check network connectivity between OpenClaw and the service
- Very specific: line 338
Verify PLANEXE_API_KEY is valid
- Distinctive: line 339
Check for typos or expired credentials
- Distinctive: line 344
Check if the service is overloaded
- Distinctive: line 345
Consider increasing the request timeout in your OpenClaw config
- Very specific: line 348
Verify Docker is installed: docker --version
- Very specific: line 349
Check container logs: docker logs <container-id>
- Very specific: line 350
Ensure port 8001 is not in use: lsof -i :8001 (macOS/Linux)
- Very specific: line 355
Check the PlanExe documentation
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
PlanExe MCP Skill
Access PlanExe's powerful planning capabilities directly from OpenClaw via the Model Context Protocol (MCP).
Overview
The planexe-mcp skill provides a unified interface to interact with PlanExe across three deployment scenarios:
- Cloud:
mcp.planexe.org(Stripe credits at home.planexe.org) - Remote Docker: Container on a separate machine
- Local Docker: Container on the same machine as OpenClaw
Setup by Deployment Scenario
Scenario A: Cloud-Hosted Service
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_accountHow to Get Your API Key:
- Visit home.planexe.org
- Purchase credits via Stripe
- Generate an API key in your account dashboard
- Copy the key to your OpenClaw configuration
Scenario B: Remote Docker
Run PlanExe in Docker on a separate machine and connect remotely.
On the Remote Machine:
docker run -p 8001:8001 planexe/planexe:latestConfiguration in OpenClaw:
PLANEXE_MCP_URL=http://<remote-ip>:8001
PLANEXE_API_KEY=your_api_keyReplace <remote-ip> with your remote machine's IP address or hostname.
Scenario C: Local Docker
Run PlanExe in Docker on the same machine as OpenClaw.
Setup:
docker run -p 8001:8001 planexe/planexe:latestConfiguration in OpenClaw:
PLANEXE_MCP_URL=http://127.0.0.1:8001
PLANEXE_API_KEY=your_api_keyInvoking PlanExe Tools
The PlanExe MCP exposes nine core tools via the /mcp endpoint:
Tool 1: example_plans
Preview 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.
Tool 2: example_prompts
Get 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.
Tool 3: plan_create
Create 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.
Tool 4: plan_status
Poll 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.
Tool 5: plan_stop
Stop 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.
Tool 6: model_profiles
Return 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.
Tool 7: plan_file_info
Retrieve 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.
Tool 8: plan_list
List 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.
Tool 9: plan_retry
Retry 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.
Typical Workflow
- Optionally call
example_plansto preview what PlanExe output looks like - Call
example_promptsto understand available planning scenarios - Optionally call
model_profilesto choose an appropriatemodel_profile - Formulate your planning prompt
- Get user approval for the request
- Call
plan_createwith your prompt and parameters → receivestask_id - Poll
plan_statusevery 5+ minutes until status iscompletedorfailed - If
failed, optionally callplan_retryto requeue with a stronger model - Call
plan_file_infowith completedtask_idto get download link - Download and use the generated plan
- If you lose a
task_id, callplan_listwith youruser_api_keyto recover it
Refer to the PlanExe API documentation for extended examples and advanced use cases.
Configuration Reference
| Variable | Example | Description |
PLANEXE_MCP_URL | https://mcp.planexe.org | MCP endpoint URL |
PLANEXE_API_KEY | pk_live_abc123... | API authentication key |
Troubleshooting
Connection Refused
- Verify
PLANEXE_MCP_URLis correct - For Docker: Ensure the container is running (
docker ps) - Check network connectivity between OpenClaw and the service
Authentication Failed
- Verify
PLANEXE_API_KEYis valid - Check for typos or expired credentials
- For cloud: Log in to home.planexe.org to verify account status
Timeout Errors
- For Docker deployments, allow time for container initialization
- Check if the service is overloaded
- Consider increasing the request timeout in your OpenClaw config
Docker Issues (Local/Remote)
- Verify Docker is installed:
docker --version - Check container logs:
docker logs <container-id> - Ensure port 8001 is not in use:
lsof -i :8001(macOS/Linux)
Support
For issues or questions:
- Check the PlanExe documentation
- Visit the PlanExe community forum
- Open an issue on GitHub
License
This skill is part of PlanExe and is available under the same license as the PlanExe project.
Other versions1 project uses a version of this.
Word for word · 1
- PlanExeOrg/PlanExe★ 402100% same
Edited · 0
None found.
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository12 other files it gives its agents.
- AGENTS.mdAGENTS.md
- database_api/AGENTS.mdAGENTS.md
- docs/AGENTS.mdAGENTS.md
- frontend_multi_user/AGENTS.mdAGENTS.md
- llm_config/AGENTS.mdAGENTS.md
- mcp_cloud/AGENTS.mdAGENTS.md
- public/AGENTS.mdAGENTS.md
- self_improve/AGENTS.mdAGENTS.md
- tools/AGENTS.mdAGENTS.md
- worker_plan/AGENTS.mdAGENTS.md
- worker_plan_database/AGENTS.mdAGENTS.md
- public/llms.txtllms.txt
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