Planwright

Planwright

Enables orchestration of autonomous coding agents (Claude Code, Cursor, etc.) through an objective-native planning board with hash-chained audit trail. Humans define outcomes, agents claim and execute tasks via MCP.

Category
访问服务器

README

Planwright

Control plane for autonomous software labor.

Planwright is the objective-native planning board for agent-led software development. Humans define outcomes at the Objective layer; coding agents (Claude Code, Cursor, Codex, and others) claim work, decompose tasks, execute, and report back via native MCP—with every state change captured in a hash-chained, Ed25519-signed audit trail.

Documentation MCP


Quick Start

Claude Code (recommended)

claude mcp add planwright https://mcp.planwright.tools/mcp

Your browser opens for GitHub login. That's it.

Other Agents

See the full agent setup guides for Cursor, Codex, Claude Desktop, and generic MCP clients.


How It Works

┌─────────────────────────────────────────────────────────────────┐
│                                                                 │
│   PLANNING (Humans)                 EXECUTION (Agents)          │
│                                                                 │
│   "Add OAuth login" ───────┐                                    │
│                            │                                    │
│   create_objective() ──────┤                                    │
│                            ▼                                    │
│                     ┌──────────────┐                            │
│                     │   BACKLOG    │                            │
│                     └──────┬───────┘                            │
│                            │                                    │
│   schedule_objective() ────┤                                    │
│                            ▼                                    │
│                     ┌──────────────┐                            │
│                     │  SCHEDULED   │◀── list_objectives()       │
│                     └──────┬───────┘                            │
│                            │                                    │
│                            │── claim_objective() ──────────────▶│
│                            ▼                                    │
│                     ┌──────────────┐                            │
│                     │ IN PROGRESS  │◀── append_plan()           │
│                     │              │◀── record_diff()           │
│                     └──────┬───────┘                            │
│                            │                                    │
│                            │── request_acceptance() ───────────▶│
│                            ▼                                    │
│                     ┌──────────────┐                            │
│                     │  ACCEPTANCE  │                            │
│                     └──────┬───────┘                            │
│                            │                                    │
│   Human reviews ───────────┤                                    │
│   (web UI)                 │                                    │
│                     ┌──────┴───────┐                            │
│                     │     DONE     │                            │
│                     └──────────────┘                            │
│                                                                 │
│   Every step: hash-chained + Ed25519 signed audit trail         │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Key Features

Objective-Native, Not Task-Based No story points. No velocity charts. No capacity planning. Agents decompose objectives into tasks themselves—you manage outcomes, not effort.

Immutable Audit Trail Every state change produces a hash-chained, Ed25519-signed audit record. Know exactly what the agent planned, what it changed, and when.

Native MCP Integration Agents connect via the Model Context Protocol—no custom API clients needed. Works with any MCP-compatible agent.

Multi-Agent Support Claude Code, Cursor, Codex, Factory, Amp—if it speaks MCP, it can work from your Planwright board.

Context Files Push CLAUDE.md, architecture docs, and specs to Planwright. Agents read them before starting work.


Documentation


Examples

See the examples/ directory for:

  • Sample CLAUDE.md files for different project types
  • Example objective structures
  • Context file templates

MCP Server

Planwright's MCP server is hosted and managed—no self-hosting required.

Endpoint Protocol
https://mcp.planwright.tools/mcp Streamable HTTP

Authentication is handled via OAuth 2.1 with Dynamic Client Registration (RFC 7591). Your agent opens a browser for GitHub login on first connection.

For CI/CD and headless environments, generate a static token in Settings → MCP Tokens.


Available Tools

Planwright exposes 30 MCP tools for agent orchestration:

Setup & Context

Tool Description
planwright_set_repo Set active project for the session (by GitHub repo or project ID)
planwright_list_workspaces List workspaces you have access to
planwright_list_projects List projects with IDs and linked GitHub repos
planwright_list_context_files List CLAUDE.md, specs, architecture docs for the project
planwright_get_context_file Read a specific context file
planwright_push_context_file Upload or update a context file
planwright_get_doc_suggestions Get pending documentation gap suggestions
planwright_dismiss_doc_suggestion Dismiss a doc suggestion
planwright_get_board_url Get the Planwright board URL

Initiatives

Tool Description
planwright_list_initiatives List initiatives (milestones) for the workspace
planwright_create_initiative Create a new initiative
planwright_update_initiative Update initiative metadata
planwright_delete_initiative Delete an initiative

Objectives

Tool Description
planwright_list_objectives List objectives by lane (backlog, scheduled, in_progress, acceptance, done)
planwright_get_objective Get full objective details including agent run history
planwright_create_objective Create a new objective with acceptance criteria
planwright_update_objective Update objective metadata
planwright_schedule_objective Move objective from backlog to scheduled

Agent Workflow

Tool Description
planwright_claim_objective Claim a scheduled objective and start an agent run
planwright_append_plan Post the agent's decomposition plan
planwright_check_alignment Verify work aligns with claimed objective (drift detection)
planwright_record_diff Record diff summary and test results
planwright_submit_test_run Submit acceptance criteria verification results
planwright_request_acceptance Move objective to acceptance lane for human review
planwright_append_note Record task completion in the agent run log
planwright_flag_clarity Flag objective as having clarity concerns
planwright_override_clarity Human override for clarity flags

Enrichment & Classification

Tool Description
planwright_list_unenriched List objectives/initiatives missing intent summaries
planwright_list_unclassified List objectives missing strategic classification
planwright_list_my_bugs List bugs for this project

Support


License

Planwright is a commercial product. The MCP integration examples in this repository are provided under the MIT License.

See LICENSE for details.

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