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URL: https://glama.ai/mcp/servers/kjm99d/MonkeyPlanner

⇱ MonkeyPlanner by kjm99d | Glama


English | 한국어 | 日本語 | 中文

MonkeyPlanner

Local-first task memory for your AI coding agents. Approve with a click; your agents do the rest. No cloud. No telemetry. Forever free, forever MIT.

Works with Claude Code · Claude Desktop · Cursor · Continue · any MCP-compatible client.

👁 MonkeyPlanner Demo

Quickstart

# Docker (recommended)
docker run -p 8080:8080 -v $(pwd)/data:/data ghcr.io/kjm99d/monkeyplanner:latest

# then wire up your agent
monkey-planner mcp install --for claude-code # or --for cursor / --for claude-desktop

Open http://localhost:8080 — the built-in Welcome board walks you through the rest.

Related MCP server: task-orchestrator

Features

Issue & Board Management

  • Kanban Board — Drag and drop, horizontal scroll, filtering, sorting, and table view toggle

  • Issue Creation — Title, markdown body, and custom properties

  • Custom Properties — Six supported types:

    • Text

    • Number

    • Select

    • Multi-select

    • Date

    • Checkbox

Approval Gate

  • Pending → Approved via a dedicated approval endpoint (cannot be done via generic PATCH)

  • Approval Queue — Bulk-approve all Pending issues across boards

  • Approved → InProgress → Done — Flexible status transitions

  • Rejected status — Record a rejection reason

Agent Features

  • Agent Instructions field — Provide detailed instructions for MCP agents to follow

  • Success Criteria — Manage completion conditions as a checklist

  • Comments — Log progress and communicate per issue

  • Dependencies — Express blocking relationships between issues

Data Visualization

  • Calendar — Monthly grid + daily activity (created, approved, completed counts)

  • Dashboard — Stats cards + weekly activity chart

  • Sidebar — Board list, issue counts, and recent items

User Experience

  • Global Search — Quick search with Cmd+K

  • Keyboard Shortcuts

    • h — Go to dashboard

    • a — Go to approval queue

    • ? — Show shortcut help

    • Cmd+S — Save

    • Escape — Close modal/dialog

  • Collapsible Sidebar — Maximize screen space

  • Dark Mode — Theme toggle

  • Internationalization — Korean, English, Japanese, and Chinese

Automation & Integrations

  • Webhooks — Discord, Slack, and Telegram support

    • Events: issue.created, issue.approved, issue.status_changed, issue.updated, issue.deleted, comment.created

  • Real-time UI sync (SSE) — Changes via MCP/CLI automatically reflect in open browser tabs, no refresh needed

  • JSON Export — Export all issue data

  • Right-click Context Menu — Quick actions

  • Issue Templates — Per-board localStorage persistence

MCP Server (AI Agent Integration)

Thirteen tools for AI agent automation:

  1. list_boards — List all boards

  2. list_issues — Query issues (filter by boardId, status)

  3. get_issue — Issue detail including instructions, criteria, and comments

  4. create_issue — Create a new issue

  5. approve_issue — Approve: Pending → Approved

  6. claim_issue — Claim: Approved → InProgress

  7. submit_qa — Submit for QA: InProgress → QA

  8. complete_issue — Complete: QA → Done (optional comment)

  9. reject_issue — Reject: QA → InProgress with required reason

  10. add_comment — Add a comment to an issue

  11. update_criteria — Check or uncheck a success criterion

  12. search_issues — Search issues by title

  13. get_version — Get the MCP server version (for diagnostics)

Tech Stack

Backend

  • Language: Go 1.26

  • Router: chi/v5

  • Database: SQLite / PostgreSQL (configurable)

  • Migrations: goose/v3

  • Embedded files: embed.FS (single-binary deployment)

Frontend

  • Framework: React 18

  • Language: TypeScript

  • Bundler: Vite 6

  • CSS: Tailwind CSS

  • State management: React Query (TanStack)

  • Drag and drop: @dnd-kit/core, @dnd-kit/sortable

  • Icons: lucide-react

  • Charts: recharts

  • i18n: react-i18next

  • Markdown: react-markdown + rehype-sanitize

MCP

  • Protocol: JSON-RPC 2.0 over stdio

  • Targets: Claude Code, Claude Desktop

Getting Started

Requirements

  • Go 1.26 or later

  • Node.js 18 or later

  • npm or yarn

Installation & Running

1. Clone and initialize

git clone https://github.com/kjm99d/MonkeyPlanner.git
cd monkey-planner
make init

2. Production build (single binary)

make build
./bin/monkey-planner

The server runs at http://localhost:8080 with the frontend embedded.

3. Development mode (separate processes)

Terminal 1 — backend:

make run-backend

Terminal 2 — frontend (Vite dev server, :5173):

make run-frontend

The frontend automatically proxies /api requests to :8080.

Environment Variables

# Server address (default: :8080)
export MP_ADDR=":8080"

# Database connection string
# SQLite (default: sqlite://./data/monkey.db)
export MP_DSN="sqlite://./data/monkey.db"

# PostgreSQL example
export MP_DSN="postgres://user:password@localhost:5432/monkey_planner"

MCP Server Setup

Recommended: auto-configure via CLI

# Claude Code (writes .mcp.json in the current directory)
monkey-planner mcp install --for claude-code

# Claude Desktop (writes the OS-native config file)
monkey-planner mcp install --for claude-desktop

# Cursor (writes .cursor/mcp.json)
monkey-planner mcp install --for cursor

Flags: --dry-run to preview, --scope user for a global entry (~/.mcp.json), --force to overwrite, --base-url <url> to point at a non-default server.

Restart the client afterwards so it re-reads the config.

Manual configuration

Works identically for Claude Code (.mcp.json), Claude Desktop (OS-native config), and Cursor (.cursor/mcp.json):

{
 "mcpServers": {
 "monkey-planner": {
 "command": "/path/to/monkey-planner",
 "args": ["mcp"],
 "env": {
 "MP_BASE_URL": "http://localhost:8080"
 }
 }
 }
}

The binary must be able to reach the HTTP server (set with MP_BASE_URL). Leave it at the default when running both on the same machine.

MCP Tool Usage Examples

AI: List all boards
→ list_boards()

AI: Find issues related to "authentication"
→ search_issues(query="authentication")

AI: Approve the first pending issue, claim it, work on it, and submit for QA
→ approve_issue() → claim_issue() → submit_qa()

Workflow — Real Usage Scenario

Below is a real workflow from fixing a language switcher bug, showing how a human and AI agent collaborate through MonkeyPlanner.

Status Flow

Pending → Approved → InProgress → QA → Done
 ↑ │ (reject with reason)
 └──────────────┘

Step-by-Step

1. Create Issue — Human finds a bug, asks AI to register it

Human: "The language selector dropdown doesn't appear when clicking the button. Create an issue."
AI: create_issue(boardId, title, body, instructions) → status: Pending

2. Approve — Human reviews and approves

Human: (clicks Approve on the board or tells AI)
AI: approve_issue(issueId) → status: Approved

3. Start Work — AI claims the issue and begins coding

AI: claim_issue(issueId) → status: InProgress
 - Reads code, identifies root cause
 - Implements fix, runs tests
 - Commits changes

4. Submit for QA — AI finishes and submits for review

AI: submit_qa(issueId, comment: "commit abc1234 — fixed click handler")
 → status: QA
 add_comment(issueId, "Commit info: ...")

5. Review — Human tests the fix

Human: Tests in browser, finds the dropdown is clipped by sidebar
 → reject_issue(issueId, reason: "Dropdown is hidden behind sidebar")
 → status: InProgress (back to step 3)

Human: Tests again after fix, everything works
 → complete_issue(issueId) → status: Done

6. Feedback Loop — Communication via comments throughout

Human: add_comment("Dropdown is clipped on the left side, fix it")
AI: get_issue() → reads comment → fixes → commit → submit_qa()
Human: Tests → complete_issue() → Done ✓

Key Takeaways

  • Human controls the gates: Approve, QA pass/reject, Complete

  • AI does the work: Code analysis, implementation, testing, commits

  • Comments are the communication channel: Both sides use add_comment to exchange feedback

  • QA loop prevents premature completion: Issues must pass human review before Done

API Reference

OpenAPI 3.0 spec: backend/docs/swagger.yaml

Key Endpoints

Boards

GET /api/boards # List boards
POST /api/boards # Create board
PATCH /api/boards/{id} # Update board
DELETE /api/boards/{id} # Delete board

Issues

GET /api/issues # List issues (filter: boardId, status, parentId)
POST /api/issues # Create issue
GET /api/issues/{id} # Issue detail + child issues
PATCH /api/issues/{id} # Update issue (status, properties, title, etc.)
DELETE /api/issues/{id} # Delete issue
POST /api/issues/{id}/approve # Approve issue (Pending → Approved)

Comments

GET /api/issues/{issueId}/comments # List comments
POST /api/issues/{issueId}/comments # Add comment
DELETE /api/comments/{commentId} # Delete comment

Properties (Custom Attributes)

GET /api/boards/{boardId}/properties # List property definitions
POST /api/boards/{boardId}/properties # Create property
PATCH /api/boards/{boardId}/properties/{propId} # Update property
DELETE /api/boards/{boardId}/properties/{propId} # Delete property

Webhooks

GET /api/boards/{boardId}/webhooks # List webhooks
POST /api/boards/{boardId}/webhooks # Create webhook
PATCH /api/boards/{boardId}/webhooks/{whId} # Update webhook
DELETE /api/boards/{boardId}/webhooks/{whId} # Delete webhook

Calendar

GET /api/calendar # Monthly stats (year, month required)
GET /api/calendar/day # Daily issue list (date required)

For full schema details, see backend/docs/swagger.yaml.

Project Structure

monkey-planner/
├── backend/
│ ├── cmd/monkey-planner/
│ │ ├── main.go # Entry point (HTTP server)
│ │ └── mcp.go # MCP server (JSON-RPC stdio)
│ ├── internal/
│ │ ├── domain/ # Domain models (Issue, Board, etc.)
│ │ ├── service/ # Business logic
│ │ ├── storage/ # Database layer (SQLite/PostgreSQL)
│ │ ├── http/ # HTTP handlers & router
│ │ └── migrations/ # goose migration files
│ ├── web/ # Embedded frontend (embed.FS)
│ ├── docs/
│ │ └── swagger.yaml # OpenAPI 3.0 spec
│ ├── go.mod
│ └── go.sum
│
├── frontend/
│ ├── src/
│ │ ├── components/ # Reusable components
│ │ ├── features/ # Page & feature components
│ │ │ ├── home/ # Dashboard
│ │ │ ├── board/ # Board & Kanban
│ │ │ ├── issue/ # Issue detail
│ │ │ ├── calendar/ # Calendar
│ │ │ └── approval/ # Approval queue
│ │ ├── api/ # API hooks & client
│ │ ├── design/ # Tailwind tokens
│ │ ├── i18n/ # Translations (en.json, ko.json, ja.json, zh.json)
│ │ ├── App.tsx # Router
│ │ ├── index.css # Global styles
│ │ └── main.tsx
│ ├── package.json
│ ├── vite.config.ts
│ ├── tsconfig.json
│ └── tailwind.config.js
│
├── .mcp.json # Claude Code MCP config
├── Makefile # Build & dev commands
├── .githooks/ # Git hooks
└── data/ # SQLite database (default)

Testing

Backend tests

make test-backend

Frontend tests

make test-frontend

Accessibility tests

make test-a11y

All tests

make test

Common Commands

# Initial setup after cloning
make init

# Production build
make build

# Run production server
./bin/monkey-planner

# Development mode
make run-backend # Terminal 1
make run-frontend # Terminal 2

# Clean build artifacts
make clean

Status Transition Rules

Pending
 ↓ (approve endpoint)
Approved
 ↓ (PATCH status)
InProgress
 ↓ (PATCH status)
Done

Pending → Approved: POST /api/issues/{id}/approve (dedicated endpoint only)
Approved ↔ InProgress ↔ Done: Free transitions via PATCH
Pending: Cannot be re-entered from other statuses
Rejected: Separate rejection state with reason tracking

License

MIT

Contributing

Issues and pull requests are welcome.

Contact

For questions or feedback about the project, please open a GitHub Issue.

A
license - permissive license
-
quality - not tested
A
maintenance

Maintenance

Maintainers
Response time
0dRelease cycle
9Releases (12mo)
Commit activity

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