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PlanExe MCP Server

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PlanExe is answering right now. Last checked 7 min ago. It exposes 11 tools. Last commit 6 Sep 2026.

MCP server for generating rough-draft project plans from natural-language prompts.

Uptime history 47 days of history · worst day 96%
47 days agonow
100.0%
Uptime 24h
92 of 92 checks
11
Tools
read from the server
492 ms
Response time
average over 24h
401
Stars
last commit 6 Sep 2026

What changed 1

Every tool that appeared, vanished or quietly changed what it asks for. Recorded since 29 August 2026. No other catalogue keeps this.

29 Aug a tool changed version

What the code does

We read the source, 21 h ago · tools taken from the live server · rules 3dff92dd89df

Capabilities

What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.

                <p class="key-hint" style="margin-top: 12px;">Maximum of 10 secrets reached. Delete an unused one to create a new one.</p>
                {% endif %}
            setTimeout(function() { btn.innerHTML = orig; btn.style.opacity = ''; }, 1500);
Reads files and sends them to the network worker_plan_database/app.py:786
        for root, _, files in os.walk(run_dir):
Sends data to a third-party sink frontend_multi_user/src/billing.py:401
    url = f"https://api.telegram.org/bot{bot_token}/createInvoiceLink"

Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.

This code can reach further than it looks

We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.

Three servers free · no card

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 7 min ago.

run in your terminal
claude mcp add planexe --transport http https://mcp.planexe.org/mcp
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "planexe": {
      "url": "https://mcp.planexe.org/mcp"
    }
  }
}
~/.codex/config.toml
[mcp_servers.planexe]
url = "https://mcp.planexe.org/mcp"
.cursor/mcp.json
{
  "mcpServers": {
    "planexe": {
      "url": "https://mcp.planexe.org/mcp"
    }
  }
}
.vscode/mcp.json
{
  "mcpServers": {
    "planexe": {
      "url": "https://mcp.planexe.org/mcp"
    }
  }
}

Available tools 11

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

plan
plan_create
Call only after example_prompts and after you have completed prompt drafting/approval (non-tool step). PlanExe turns the approved prompt into a strategic project-plan draft (20+ sections) in ~10-20 min. Sections include: executive summary, interactive Gantt charts, investor pitch, project plan with SMART criteria, strategic decision analysis, scenario comparison, assumptions with expert review, governance structure, SWOT analysis, team role profiles, simulated expert criticism, work breakdown structure, plan review (critical issues, KPIs, financial strategy, automation opportunities), Q&A, premortem with failure scenarios, self-audit checklist, and adversarial premise attacks that argue against the project. The adversarial sections (premortem, self-audit, premise attacks) surface risks and questions the prompter may not have considered. Returns plan_id (UUID); use it for plan_status, plan_stop, plan_retry, and plan_file_info. To track progress, poll plan_status at reasonable intervals (e.g. every 5 minutes). Optionally, run `curl -N <sse_url>` in a background shell as a completion detector — the stream auto-closes on terminal state (completed/failed/stopped). If you lose a plan_id, call plan_list to recover it. If the same prompt + model_profile is submitted by the same user within a short window, the existing plan is returned (with deduplicated=true) instead of creating a new one. If you are unsure which model_profile to choose, call model_profiles first. If your deployment uses credits, include user_api_key to charge the correct account. Common error codes: INVALID_USER_API_KEY, USER_API_KEY_REQUIRED, INSUFFICIENT_CREDITS.
plan_file_info
Returns file metadata (content_type, download_url, download_size, expires_at) for the report or zip artifact. Use artifact='report' (default) for the interactive HTML report (~700KB, self-contained with embedded JS for collapsible sections and interactive Gantt charts — open in a browser). Use artifact='zip' for the full pipeline output bundle (md, json, csv intermediary files that fed the report). While the task is still pending or processing, returns {ready:false,reason:"processing"}. Check readiness by testing whether download_url is present in the response. Once ready, present download_url to the user or fetch and save the file locally. Download URLs expire after 15 minutes (see expires_at); call plan_file_info again to get a fresh URL if needed. Terminal error codes: generation_failed (plan failed), content_unavailable (artifact missing). Unknown plan_id returns error code PLAN_NOT_FOUND.
plan_list
List the most recent plans for an authenticated user. Returns up to `limit` plans (default 10, max 50) newest-first, each with plan_id, state, progress_percentage, created_at (ISO 8601), and a prompt_excerpt (first 100 chars). Use this to recover a lost plan_id or to review recent activity.
plan_resume
Resume a failed or stopped plan without discarding completed intermediary files. Plan generation restarts from the first incomplete step, skipping all steps that already produced output files. Use plan_resume when plan_status shows 'failed' or 'stopped' and plan generation was interrupted before completing all steps (network drop, timeout, plan_stop, worker crash). For a full restart or to change model_profile, use plan_retry instead. Only failed or stopped plans can be resumed. Returns PLAN_NOT_FOUND when plan_id is unknown and PLAN_NOT_RESUMABLE when the plan is not in failed or stopped state. Returns PIPELINE_VERSION_MISMATCH when the snapshot was created by a different pipeline version; use plan_retry instead.
plan_retry
Retry a plan that is currently in failed or stopped state. Pass the plan_id and optionally model_profile (defaults to baseline). The plan is reset to pending, prior artifacts are cleared, and the same plan_id is requeued for processing. Returns PLAN_NOT_FOUND when plan_id is unknown and PLAN_NOT_FAILED when the plan is not in failed or stopped state.
plan_status
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 .
plan_stop
Request the plan generation to stop. Pass the plan_id (the UUID returned by plan_create). Stopping is asynchronous: the stop flag is set immediately but the plan may continue briefly before halting. A stopped plan will transition to the stopped state. If the plan is already completed or failed, stop_requested returns false (the plan already finished). Unknown plan_id returns error code PLAN_NOT_FOUND.
example
example_plans
Returns a curated list of example plans with download links for reports and zip bundles. Use this to preview what PlanExe output looks like before creating your own plan. Especially useful when the user asks what the output looks like before committing to a plan. No API key required.
example_prompts
Call this first. Returns example prompts that define what a good prompt looks like. Do NOT call plan_create yet. Optional before plan_create: call model_profiles to choose model_profile. Next is a non-tool step: formulate a detailed prompt (typically ~300-800 words; use examples as a baseline, similar structure) and get user approval. Good prompt shape: objective, scope, constraints, timeline, stakeholders, budget/resources, and success criteria. Write the prompt as flowing prose, not structured markdown with headers or bullet lists. Weave technical specs, constraints, and targets naturally into sentences. Include banned words/approaches and governance preferences inline. The examples demonstrate this prose style — match their tone and density. Then call plan_create. PlanExe is not for tiny one-shot outputs like a 5-point checklist; and it does not support selecting only some internal pipeline steps.
model
model_profiles
Optional helper before plan_create. Returns model_profile options with plain-language guidance and currently available models in each profile. If no models are available, returns error code MODEL_PROFILES_UNAVAILABLE.
send
send_feedback
Submit feedback about PlanExe — issues, impressions, or suggestions. Callable at any point in the workflow; fire-and-forget, never blocks. Use category to classify: mcp (MCP tools, SSE, plan_status, workflow), plan (the generated output files), code (PlanExe source), docs (documentation), other. Optionally attach to a plan via plan_id. Use rating (1-5) for sentiment: 1=strong negative, 3=neutral, 5=strong positive. Especially useful for reporting: SSE streams that close before plan completion, plan_status returning stale or inconsistent data, queue delays where workers are slow to pick up plans, and impressions of plan output quality after reviewing reports. Include specific details (plan_id, percentages, timestamps) when reporting issues.

Endpoints

URLTransportStateLatencyChecked
https://mcp.planexe.org/mcp streamable-http answering 360 ms 7 min ago

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PlanExe — questions

Answers built from our own checks of this server.

What can PlanExe do?
It exposes 11 tools, read directly from the server on our last check. Among them: example_plans, example_prompts, model_profiles, plan_create, plan_file_info, plan_list and 5 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
What is PlanExe mostly used for?
Its tools cluster around plan and example. That is what this server is built to work with — the grouping comes from the actual tool names, not from a category we assigned.
Is PlanExe working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 92 of 92 checks got a reply (100.0%), average response time 492 ms. The bar chart above shows every period we have measured.
How do I connect PlanExe?
Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does PlanExe need an API key?
No. PlanExe completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 11 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is PlanExe?
It answers our handshake in 492 ms on average, which is faster than 29% of all working MCP servers we measure. The comparison comes from our own checks across the whole registry, every 15 minutes.
Is PlanExe open source?
Yes — it is published under the MIT licence, written in Python and 401 stars on GitHub. The source link is on this page, so you can read exactly what it does with your data before you connect it.