PlanExe

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

CommunityOnlineAPI key requiredGlobalFreeRead-only

What it can do

    What data it sees

    Do you need an account

    An API key from the service settings is required

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

    How to connect

    How to connect PlanExe to Claude

    The server address to paste into Claude settings:

    https://mcp.planexe.org/mcp

    1. Open Claude (claude.ai or the desktop app).
    2. Go to Settings → Connectors.
    3. Click Add custom connector.
    4. Paste the server address copied below into Remote MCP server URL and click Add.
    5. In a chat, click + → Connectors and switch the new connector on.

    Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

    Authorization

    After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

    How to connect PlanExe to ChatGPT

    The server address to paste into ChatGPT settings:

    https://mcp.planexe.org/mcp

    1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
    2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
    3. Open Settings → Connectors and click Create.
    4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
    5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
    6. In a new chat open + → Apps/Connectors and enable the connector.

    OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

    Authorization

    On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

    How to connect PlanExe to Cursor

    The server address to paste into Cursor settings:

    https://mcp.planexe.org/mcp

    Fastest: click Open in Cursor below and confirm the prompt.

    Manually:

    1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
    2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
    3. Save the file. The server appears in the MCP list; authorize it there if asked.

    Authorization

    If the service needs sign-in, an authorize button appears next to the server in the MCP list.

    Server tool list (11)

    Raw names from tools/list. Only developers need these.

    example_plansReturns 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_promptsCall 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_profilesOptional 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.
    plan_createCall 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_statusReturns 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 li
    plan_stopRequest 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.
    plan_retryRetry 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_resumeResume 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_file_infoReturns 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_listList 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.
    send_feedbackSubmit 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.