polyplan-mcp

polyplan-mcp

Enables structured multi-model AI planning sessions across multiple CLI coding tools, orchestrating independent planning, peer review, and final synthesis.

Category
访问服务器

README

PolyPlan MCP

A multi-model AI planning MCP server for CLI coding tools

npm version npm downloads License: MIT Node.js Version

What is PolyPlan?

PolyPlan is a project-local MCP (Model Context Protocol) server that enables developers to run structured, multi-model AI planning sessions across multiple CLI coding tools simultaneously.

When developers use multiple AI models (Claude Code, Copilot, OpenCode, Codex, Cursor, AntiGravity) for planning, PolyPlan provides a structured way to collect, cross-review, and synthesize plans from all models into one final implementable master plan.

How It Works

PolyPlan orchestrates a 3-round planning workflow:

  • Round 1: Each model creates an independent plan saved to .plans/ without seeing what any other model thinks.
  • Round 2: Each model reads all other models' Round 1 plans (except its own) and creates a revised master plan informed by peer review.
  • Final: One chosen model (your strongest, e.g., Opus 4.6 or GPT-4o) reads ALL Round 1 + Round 2 plans and produces the final, synthesized, implementable plan.

Installation

npm install -g polyplan-mcp

Note: Global install does best-effort user-level MCP registration. Run polyplan-mcp init inside each project to create project-local MCP config and slash-command wrappers.

Quick Start

  1. Initialize in your project
    polyplan-mcp init
    
  2. Restart your AI coding CLI/editor
  3. In any connected CLI tool, start Round 1
    /polyplan round1 [your problem]
    
    Or use the direct command:
    /round_1 [your problem]
    
  4. Check status anytime
    /polyplan status
    
    Or:
    /show_status
    

All Commands

These are the MCP tool names and the generated slash wrapper names. PolyPlan keeps MCP tools as the execution layer and creates client-specific prompt/command files during polyplan-mcp init where supported.

Slash Command Description
/round_1 Start Round 1 — create an independent plan (no other models seen).
/round_1_context Get the Round 1 prompt — call before generating your plan.
/round_2 Start Round 2 — peer-review all Round 1 plans and write a revised master plan. Requires ≥2 Round 1 plans.
/round_2_context Get the Round 2 prompt with all other models' plans injected.
/final_plan Start Final round — synthesize ALL plans into one implementable plan. Requires ≥1 Round 2 plan.
/final_plan_context Get the Final round prompt with all Round 1 + Round 2 plans injected.
/show_status Show full session state — which models completed each round.
/clear_plans Delete plan files. Specify all, round1, round2, or final. Requires confirm=true.
/show_conflicts Show all points where models disagreed in Round 1.
/show_questions Show all open questions raised by any model, and which were answered by others.
/show_diff Show what changed for a specific model between Round 1 and Round 2.
/show_agree Show what ALL models agreed on independently in Round 1.
/show_summary One-paragraph summary of each model's plan.
/export_plans Bundle entire .plans/ session into one readable markdown file for sharing.
/show_history Full audit log — model, CLI tool, round, time, action.

PolyPlan also generates /polyplan as a routing command where client command files are supported:

/polyplan status
/polyplan round1 [problem]
/polyplan round2
/polyplan final

File Naming Convention

All plans are stored locally in the .plans/ directory using the following convention:

.plans/
  round1-copilot-sonnet4.6.md
  round1-claudecode-kimik2.md
  round2-opencode-gemini2.5.md
  final-antigravity-opus4.6.md

Supported CLI Tools

PolyPlan connects to any tool supporting the Model Context Protocol (MCP):

  • Claude Code (.mcp.json, .claude/commands/)
  • Cursor (.cursor/mcp.json, .cursor/commands/)
  • OpenCode (opencode.json, .opencode/commands/)
  • VS Code / GitHub Copilot Agent mode (.vscode/mcp.json, .github/prompts/)
  • GitHub Copilot CLI (.mcp.json)
  • Codex CLI (global ~/.codex/config.toml best-effort postinstall registration)
  • Windsurf / AntiGravity / Gemini CLI workflows (MCP tools and prompts where configured)

See COMPATIBILITY.md for the full client support matrix and limitations.

CLI Tool Specific Usage

Always pass your model name

PolyPlan saves plans using the model name in the filename (e.g., round1-claudecode-sonnet4.6.md). Auto-detection is not always possible, so always pass modelName explicitly:

Call the round_1 tool with modelName='sonnet4.6', problemDescription='...', and plan='...'

OpenCode / Gemini

OpenCode with Gemini models requires explicit tool invocation syntax. Use "Call the tool" instead of "Use polyplan":

❌ May not invoke the tool ✅ Always invokes the tool
Use polyplan round1 for this problem: ... Call the round_1 tool with modelName='gemini2.5' and problem='...' and plan='...'
Use polyplan to show status Call the show_status tool
Use polyplan round2 Call the round_2_context tool, then call round_2

The word "Call" forces direct MCP tool invocation in OpenCode/Gemini instead of a natural-language response.

Claude Code

Claude Code auto-detects as claudecode but does not expose the active model name via MCP. Always pass modelName explicitly:

/round_1 modelName=sonnet4.6 problemDescription="..." plan="..."

GitHub Copilot (VS Code)

Works with slash commands directly. Still recommended to pass modelName:

/round_1 with modelName='sonnet4.6' for this problem: ...

Why Multi-Model Planning?

Different models have different strengths, blind spots, and reasoning styles. Some excel at architectural structure, while others are better at catching security edge cases. By using 5+ models independently and then cross-reviewing, you catch more issues, resolve conflicts early, and produce a significantly more robust plan than any single model could produce alone.

Contributing

We welcome contributions! Please see our CONTRIBUTING.md for details on how to get started, run tests, and submit Pull Requests.

License

MIT © 2026 PolyPlan Contributors

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选