Planning System MCP Server
This MCP server enables AI agents to interact with a comprehensive planning system for project management through the following capabilities:
Plan Management Create, update, delete, and list plans with status tracking (draft, active, completed, archived). Retrieve full hierarchical structure or comprehensive summaries of plans.
Node Operations (Phases, Tasks, Milestones) Create nodes with rich metadata (title, description, type, status, due dates, agent instructions, acceptance criteria). Update, delete, move, and reorder nodes within the hierarchy. Get full node context (children, logs, artifacts) or ancestry path from root to a specific node. Batch update multiple nodes in a single operation.
Universal Search Query across global, plan, or node scopes with filters for type, status, and result limits.
Logging & Collaboration Add and retrieve log entries (progress, reasoning, challenge, decision, comment) with tagging and filtering support.
Artifact Management Add, get, search, list, and batch retrieve artifacts associated with nodes, including metadata and content type support.
Dependency Analysis Create dependency edges with cycle detection, list dependencies, analyze impact scenarios, and identify critical paths.
Progressive Context Loading Retrieve task context at configurable depth levels (1–4) with token budgeting for efficient agent context management, plus dependency-aware task suggestions.
RPI Chains Create structured Research→Plan→Implement task decompositions with automatic dependency wiring.
Transport Flexibility Supports stdio mode for local use and HTTP/SSE mode for remote or cloud deployment.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Planning System MCP Serversearch for all active tasks in the product launch plan"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AgentPlanner MCP Server
MCP server for AgentPlanner — AI agent orchestration with planning, dependencies, knowledge graphs, and human oversight. Works with Claude Desktop, Claude Code, ChatGPT, Cursor, Windsurf, Cline, and any MCP-compatible client.
Prerequisites
An AgentPlanner account at agentplanner.io
An API token (Settings > API Tokens in the AgentPlanner UI)
Setup
Claude Desktop
Add to your claude_desktop_config.json:
{
"mcpServers": {
"agent-planner": {
"command": "npx",
"args": ["-y", "agent-planner-mcp"],
"env": {
"USER_API_TOKEN": "your-token",
"API_URL": "https://agentplanner.io/api"
}
}
}
}Config location: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) | %APPDATA%\Claude\claude_desktop_config.json (Windows)
Claude Code
claude mcp add agent-planner -- npx -y agent-planner-mcpThen set the env vars USER_API_TOKEN and API_URL=https://agentplanner.io/api.
ChatGPT
Settings > Apps > Advanced > Developer mode
Add MCP Server > URL:
https://agentplanner.io/mcpAuth type: API Key > enter your token from agentplanner.io Settings
Cursor
Add to .cursor/mcp.json in your project root:
{
"mcpServers": {
"agent-planner": {
"command": "npx",
"args": ["-y", "agent-planner-mcp"],
"env": {
"USER_API_TOKEN": "your-token",
"API_URL": "https://agentplanner.io/api"
}
}
}
}Windsurf
Add to ~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"agent-planner": {
"command": "npx",
"args": ["-y", "agent-planner-mcp"],
"env": {
"USER_API_TOKEN": "your-token",
"API_URL": "https://agentplanner.io/api"
}
}
}
}Cline (VS Code)
Add the same JSON config to your Cline MCP settings in VS Code.
Any HTTP MCP Client
Endpoint:
https://agentplanner.io/mcpDiscovery:
https://agentplanner.io/.well-known/mcp.jsonAuth header:
Authorization: ApiKey <your-token>Transport: Streamable HTTP (MCP 2025-03-26)
Key Features
60+ tools for planning, task management, dependencies, and knowledge
Dependency graph with cycle detection, impact analysis, and critical path
Progressive context — 4-layer context assembly with token budgeting
Knowledge graph — temporal knowledge via Graphiti (entities, facts, contradictions)
RPI chains — Research > Plan > Implement task decomposition
Goal tracking — health dashboard, briefings, bottleneck detection
Task claims — TTL-based locking for multi-agent coordination
Organizations — multi-tenant isolation
Available Tools
Planning & Search
search- Universal search across all scopes with filterscreate_plan/update_plan/delete_plan- Plan CRUDget_plan_structure- Hierarchical plan treeget_plan_summary- Statistics and summary
Node Management
create_node/update_node/delete_node- Node CRUDmove_node- Reorder or reparent nodesbatch_update_nodes- Update multiple nodes at onceget_node_context/get_node_ancestry- Rich context
Dependencies & Analysis
create_dependency/delete_dependency- Manage edgeslist_dependencies/get_node_dependencies- Query graphanalyze_impact- Delay/block/remove scenario analysisget_critical_path- Longest blocking chaincreate_rpi_chain- Research > Plan > Implement chain
Progressive Context
get_task_context- Primary context tool (depth 1-4, token budget)suggest_next_tasks- Dependency-aware suggestionsget_agent_context/get_plan_context- Focused views
Knowledge Graph
add_learning/recall_knowledge- Learn and retrievefind_entities/check_contradictions- Graph queriesget_recent_episodes- Temporal episodes
Goals & Organizations
create_goal/update_goal/list_goals/get_goal- Goal managementcheck_goals_health- Health dashboardcreate_organization/get_organization/list_organizations/update_organization
Collaboration
add_log/get_logs- Log entries (comments, progress, reasoning)claim_task/release_task- Task lockingshare_plan- Collaboration management
Alignment & Review
check_coherence_pending- See which plans/goals need alignment review (staleness check)run_coherence_check- Evaluate plan quality and stamp as reviewed
LLM Skill Reference
See SKILL.md for a complete reference designed to be consumed by LLMs. Include it in system prompts or agent configurations to give any LLM full knowledge of how to use AgentPlanner tools effectively.
See AGENT_GUIDE.md for a quick reference card.
Transport Modes
stdio (default)
For local use with Claude Desktop, Claude Code, Cursor, Windsurf, Cline:
npx agent-planner-mcpHTTP/SSE
For remote access (ChatGPT, cloud deployments, multi-agent systems):
MCP_TRANSPORT=http npx agent-planner-mcp
# Listens on http://127.0.0.1:3100Production endpoint: https://agentplanner.io/mcp
See HTTP_MODE.md for details.
Local Development
git clone https://github.com/TAgents/agent-planner-mcp.git
cd agent-planner-mcp
npm install
npm run setup # Interactive setup wizard
npm run dev # Dev server with hot reloadEnvironment Variables
Variable | Description | Default |
| AgentPlanner API URL |
|
| API token (required) | — |
|
|
|
| HTTP mode port |
|
| Environment |
|
License
MIT License - see LICENSE for details.
Support
CHANGELOG.md for version history
PDR.md for technical design
Resources
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