PlanExeOrgのアバター

PlanExe MCPサーバー

  • GitHubスター 403
  • 2026年11月8日更新

自然言語のプロンプトから、たたき台のプロジェクト計画を生成するMCPサーバーです。

PlanExe MCPとは?

自然言語のプロンプトから、たたき台のプロジェクト計画を生成するMCPサーバーです。PlanExeはオープンソースのツールで、AIエージェント向けの最高峰の計画ツールです。平易な英語で書いた1つの目標文から、ローカルまたはクラウドのモデルを使って、約15分で40ページの戦略的な計画を作成します。アウトラインを素早く作るためのアクセラレーターですが、洗練された計画を一発で作る万能薬ではありません。典型的な出力には次のものが含まれます:io.github.PlanExeOrg/planexe、X-API-Keyを含む11個のMCPツールを公開しています。Claude Desktop、Cursor、またはMCP互換の任意のクライアントから、アップストリームのサーバーマニフェストを使ってインストールします。

数字で見るPlanExe

  • 検証済みツール11個を提供(MCPイントロスペクション)
  • 2026年11月8日時点のデータ(Skillselionカタログ同期)
ターミナル
claude mcp add --transport http planexe https://mcp.planexe.org/mcp --header "X-API-Key: YOUR_X_API_KEY"

PlanExeのバッジを追加する

このMCPサーバーがSkillselionに掲載されていることを開発者に示せます。READMEに貼り付けてください。

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最終更新2026年11月8日
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ユースケースと機能

PlanExeをMCPクライアントに接続するにはどうすればよいですか?

自然言語のプロンプトから、たたき台のプロジェクト計画を生成するMCPサーバーです。

PlanExe MCPはどんな人に向いていますか?

AI・LLMツール向けに、PlanExeをClaude、Cursor、またはカスタムエージェントに組み込むチームに向いています。

見送ったほうがよい場合: stdio/SSEトランスポートのないMCP以外のSDKやホスト型APIが必要な場合は、見送ってください。

PlanExe MCPは何を生成しますか?

ツールの登録とクライアントの設定を検証済みの、動作するPlanExe MCPサーバー。

  • 自然言語の説明から生成される、ラフな下書きのプロジェクト計画テキスト
  • PRDやチケットのバックログに編集して使える、構造化された計画のセクション
  • 要件が変わったときに再スコープするための、再現可能なエージェントのワークフロー
  • [object Object]
概要

PlanExeで何ができますか?

機能
planexe:MCPツールの登録 · planexe:クライアント設定 · planexe:エージェントのワークフロー連携
ユースケース
オーケストレーション
実行環境
リモートサーバー
ドキュメントより

PlanExeのドキュメントが説明する機能

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

PlanExeはどんなツールを提供しますか? 11

公開されているツールのメタデータです。このサーバーがエージェントのために何をできるかを示します。

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_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.

plan_createパラメーター3個

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

  • promptstring
  • start_dateOptional plan start date in ISO 8601 format with timezone offset (e.g. '2025-06-15T09:00:00+02:00'). When omitted, the plan starts now. Use this to set a past or future start date for the plan.
  • model_profilestringModel profile: baseline, premium, frontier, custom. Call model_profiles to inspect options.
plan_statusパラメーター1個

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 tim

  • plan_idstringPlan UUID returned by plan_create.
plan_stopパラメーター1個

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.

  • plan_idstringPlan UUID returned by plan_create. Use it to stop the plan creation.
plan_retryパラメーター2個

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_idstringUUID of the failed plan to retry.
  • model_profilestringModel profile used for retry. Defaults to baseline.
plan_resumeパラメーター2個

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_idstringUUID of the failed plan to resume.
  • model_profilestringModel profile used for the resumed plan. Defaults to baseline.
plan_file_infoパラメーター2個

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. If your client exposes plan_download (e.g. mcp_local), prefer that to save the file locally. Terminal error codes: generation_failed (plan failed), content_unavailable (artifact missing). Unknown plan_id returns error code PLAN_NOT_FOUND.

  • plan_idstringPlan UUID returned by plan_create. Use it to download the created plan.
  • artifactstringDownload artifact type: report or zip.
plan_listパラメーター1個

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.

  • limitintegerMaximum number of plans to return (1–50). Newest plans are returned first.
send_feedbackパラメーター4個

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.

  • ratingSentiment: 1=strong negative, 2=weak negative, 3=neutral, 4=weak positive, 5=strong positive.
  • messagestringFree-text feedback. Include environment context if reporting an issue.
  • plan_idOptional plan UUID to attach this feedback to.
  • categorystringFeedback category: mcp, plan, code, docs, or other.

PlanExeのREADMEには何が書かれていますか?

README.md

The PlanExe icon is the P character and E character

Turn your idea into a comprehensive plan in minutes, not months.

Try PlanExe in your browser — generate a free plan

Describe your idea, hit submit, and PlanExe returns a ~40-page plan in about 15 minutes.

Create an account  |  See example plans  |  Getting started guide


Example plans generated with PlanExe

What is PlanExe?

PlanExe is an open-source tool and the premier planning tool for AI agents. It turns a single plain-english goal statement into a 40-page, strategic plan in ~15 minutes using local or cloud models. It's an accelerator for outlines, but no silver bullet for polished plans.

Typical output contains:

  • Executive summary
  • Gantt chart
  • Governance structure
  • Role descriptions
  • Stakeholder maps
  • Risk registers
  • SWOT analyses

PlanExe produces well-structured, domain-aware output: correct terminology, logical task sequencing, and coherent sections. For technical topics (engineering programs, regulated industries), it often gets the vocabulary and structure right. Think of it as a first-draft scaffold that gives you something concrete to critique and refine.

However, the output has consistent weaknesses that matter: budgets are assumed rather than derived, timeline estimates are not grounded in real resource constraints, risk mitigations tend toward generic advice, and legal/regulatory details are plausible-sounding but unverified. The output should be treated as a structured starting point, not a deliverable. How much work it saves depends heavily on the project. For brainstorming or a first outline, it can save hours. For a client-ready plan, expect significant rework on every number, timeline, and risk section.


Model Context Protocol (MCP)

PlanExe exposes an MCP server for AI agents at https://mcp.planexe.org/

Assuming you have an MCP-compatible client (Claude, Cursor, Codex, LM Studio, Windsurf, OpenClaw, Antigravity).

The Tool workflow

  1. example_plans (optional, preview what PlanExe output looks like)
  2. example_prompts
  3. model_profiles (optional, helps choose model_profile)
  4. non-tool step: draft/approve prompt
  5. plan_create
  6. plan_status (poll every 5 minutes until done)
  7. optional if failed: plan_retry
  8. download the result via plan_file_info

Concurrency note: each plan_create call returns a new plan_id; server-side global per-client concurrency is not capped, so clients should track their own parallel plans.

Option A: Remote MCP (fastest path)
Prerequisites
  • An account at https://home.planexe.org.
  • Sufficient funds to create plans.
  • A PlanExe API key (pex_...) from your account

Use this endpoint directly in your MCP client:

{
  "mcpServers": {
    "planexe": {
      "url": "https://mcp.planexe.org/mcp",
      "headers": {
        "X-API-Key": "pex_your_api_key_here"
      }
    }
  }
}
Option B: Run MCP server locally with Docker
Prerequisites
  • Docker
  • OpenRouter account
  • Create a PlanExe .env file with OPENROUTER_API_KEY.

Start the full stack:

docker compose up --build

Make sure that you can create plans in the web interface, before proceeding to MCP.

Then connect your client to:

  • http://localhost:8001/mcp

For local docker defaults, auth is disabled in docker-compose.yml.

MCP docs

Running the Pipeline via CLI (no frontend)

If you have a local Python environment set up and want to invoke the pipeline directly — without the Flask UI — use the planexe CLI script at the repo root.

# Create a plan from a text prompt
./planexe create_plan \
    --plan-text "Small coffee shop in Copenhagen, Denmark" \
    --output-dir ./planexe-outputs/1984-12-31/MyCoffeeShop_v1

# Or: read the plan prompt from a file
./planexe create_plan \
    --plan-file my_plan.txt \
    --output-dir ./planexe-outputs/1984-12-31/MyCoffeeShop_v1

The script creates the run directory if it does not exist and writes:

File Contents
start_time.json {"server_iso_utc": "<current UTC ISO timestamp>"}
plan.txt The plain-text plan prompt

For AI Agents

PlanExe is designed as infrastructure for AI agents. If you are an AI agent reading this:

  • Discovery: Read public/llms.txt for service metadata, MCP tools, and endpoints.
  • MCP endpoint: https://mcp.planexe.org/mcp — connect with an API key from home.planexe.org.
  • Workflow: Call example_prompts to learn the expected prompt format, draft a detailed prompt (~300-800 words of flowing prose), then call plan_create.
  • Agent guide: See docs/mcp/autonomous_agent_guide.md for the complete autonomous workflow.
  • Key outputs in zip: wbs_level1.json (work packages), wbs_level2.json (tasks), pre_project_assessment.json (feasibility).

Run locally with Docker (Click to expand)

Prerequisite: Docker with Docker Compose installed; you only need basic Docker knowledge. No local Python setup is required because everything runs in containers.

Follow these steps
  1. Clone the repo and enter it:
git clone https://github.com/PlanExeOrg/PlanExe.git
cd PlanExe
  1. Provide an LLM provider. Copy .env.docker-example to .env and fill in OPENROUTER_API_KEY with your key from OpenRouter. The containers mount .env and llm_config/; pick a model profile there. For host-side Ollama, use the docker-ollama-llama3.1 entry and ensure Ollama is listening on http://host.docker.internal:11434.

  2. Start the stack (first run builds the images):

docker compose up worker_plan frontend_multi_user

The worker listens on http://localhost:8000 and the UI comes up on http://localhost:5001 after the Postgres and worker healthchecks pass.

  1. Open http://localhost:5001 in your browser, create an account (or log in with the admin credentials from .env), enter your idea, and watch progress with:
docker compose logs -f worker_plan

Outputs are written to run/ on the host (mounted into both containers).

  1. Stop with Ctrl+C (or docker compose down). Rebuild after code/dependency changes:
docker compose build --no-cache worker_plan frontend_multi_user

For compose tips, alternate ports, or troubleshooting, see docs/docker.md or docker-compose.md.

Configuration

Config A: Run a model in the cloud using a paid provider. Follow the instructions in OpenRouter.

Config B: Run models locally on a high-end computer. Follow the instructions for either Ollama or LM Studio. When using host-side tools with Docker, point the model URL at the host (for example http://host.docker.internal:11434 for Ollama).

Recommendation: I recommend Config A as it offers the most straightforward path to getting PlanExe working reliably.


Help (Click to expand)

For help or feedback.

Join the PlanExe Discord.

GitHubスター順に並べたClaude Code MCPサーバーの一覧はこちらです。

PlanExe MCPに関連するMCPサーバーは?

PlanExeは類似ツールとどう違いますか?

リモートで計画のたたき台を作るMCP APIです。ローカルのブレインストーミングスキルや、課題管理ツールからのガントチャートのエクスポートではありません。

PlanExe MCPのよくある質問

PlanExe MCPは何をするものですか?

自然言語のプロンプトから、たたき台のプロジェクト計画を生成するMCPサーバーです。

PlanExe MCPはどんなときに使うべきですか?

PlanExe MCP mcpや、自然言語のプロンプトからたたき台のプロジェクト計画を生成するMCPサーバーについて尋ねるとき。

このMCPサーバーはインストールしても安全ですか?

本番環境にインストールする前に、このページのセキュリティ監査パネルを確認してください。

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