Plane Agent

CLI or API | MCP | Agent

Version: 0.28.0

Overview

Plane Agent is a production-grade Agent and Model Context Protocol (MCP) server designed to interface directly with Plane MCP Agent.

Key Features

Consolidated Action-Routed MCP Tools: Minimizes token overhead and eliminates tool bloat in LLM contexts by grouping methods into optimized, togglable tool modules.
Enterprise-Grade Security: Comprehensive support for Eunomia policies, OIDC token delegation, and granular execution context tracking.
Integrated Graph Agent: Built-in Pydantic AI agent supporting the Agent Control Protocol (ACP) and standard Web interfaces (AG-UI).
Native Telemetry & Tracing: Out-of-the-box OpenTelemetry exports and native Langfuse tracing.

CLI or API

This agent wraps the Plane MCP Agent API. You can interact with it programmatically or via its integrated execution entrypoints.

Detailed instructions on how to use the underlying API wrappers, extended schema bindings, and developer SDK references are maintained in docs/index.md.

MCP

This server utilizes dynamic Action-Routed tools to optimize token overhead and maximize IDE compatibility.

Available MCP Tools

Tool Module	Toggle Env Var	Enabled by Default	Description & Nested Methods
Projects	`PROJECTS_TOOL`	`True`	Manage plane projects operations. Action-routed methods: `list_projects`, `retrieve_project`.
Work Items	`WORK_ITEMS_TOOL`	`True`	Manage plane work items operations. Action-routed methods: `create_work_item`, `create_work_item_comment`, `create_work_item_link`, `create_work_log`, `delete_work_item`, `list_work_item_activities`, `list_work_item_comments`, `list_work_item_links`, `list_work_item_relations`, `list_work_item_types`, `list_work_items`, `list_work_logs`, `retrieve_work_item`, `retrieve_work_item_by_identifier`, `search_work_items`, `update_work_item`.
Cycles	`CYCLES_TOOL`	`True`	Manage plane cycles operations. Action-routed methods: `add_work_items_to_cycle`, `create_cycle`, `delete_cycle`, `list_cycle_work_items`, `list_cycles`, `retrieve_cycle`, `update_cycle`.
Epics	`EPICS_TOOL`	`True`	Manage plane epics operations. Action-routed methods: `create_epic`, `delete_epic`, `list_epics`, `retrieve_epic`, `update_epic`.
Milestones	`MILESTONES_TOOL`	`True`	Manage plane milestones operations. Action-routed methods: `create_milestone`, `delete_milestone`, `list_milestones`, `retrieve_milestone`, `update_milestone`.
Modules	`MODULES_TOOL`	`True`	Manage plane modules operations. Action-routed methods: `create_module`, `delete_module`, `list_modules`, `retrieve_module`, `update_module`.
States	`STATES_TOOL`	`True`	Manage plane states operations. Action-routed methods: `create_state`, `list_states`.
Users	`USERS_TOOL`	`True`	Manage plane users operations. Action-routed methods: `get_me`, `list_users`.
Workspaces	`WORKSPACES_TOOL`	`True`	Manage plane workspaces operations. Action-routed methods: `get_workspace`, `get_workspace_features`, `get_workspace_members`, `update_workspace_features`.
Initiatives	`INITIATIVES_TOOL`	`True`	Manage plane initiatives operations. Action-routed methods: `create_initiative`, `list_initiatives`.
Intake	`INTAKE_TOOL`	`True`	Manage plane intake operations. Action-routed methods: `create_intake_work_item`, `list_intake_work_items`.
Labels	`LABELS_TOOL`	`True`	Manage plane labels operations. Action-routed methods: `create_label`, `list_labels`.
Pages	`PAGES_TOOL`	`True`	Manage plane pages operations. Action-routed methods: `create_project_page`, `retrieve_project_page`.

Detailed tool schemas, parameter shapes, and validation constraints are preserved in docs/index.md.

Dynamic Tool Selection & Visibility

This MCP server supports dynamic toolset selection and visibility filtering at runtime. This allows you to restrict the set of exposed tools in order to prevent blowing up the LLM's context window.

You can configure tool filtering via multiple input channels:

CLI Arguments: Pass --tools or --toolsets (or their disabled counterparts --disabled-tools and --disabled-toolsets) during startup.
Environment Variables: Define standard environment variables:
- MCP_ENABLED_TOOLS / MCP_DISABLED_TOOLS
- MCP_ENABLED_TAGS / MCP_DISABLED_TAGS
HTTP SSE Request Headers: Pass custom headers during transport initialization:
- x-mcp-enabled-tools / x-mcp-disabled-tools
- x-mcp-enabled-tags / x-mcp-disabled-tags
HTTP SSE Request Query Parameters: Append query parameters directly to your transport connection URL:
- ?tools=tool1,tool2
- ?tags=tag1

When query strings or parameters are supplied, an LLM-free Knowledge Graph resolution layer (using DynamicToolOrchestrator) matches query intents against known tool tags, names, or descriptions, with safe fallback and automated 24-hour background cache refreshing.

MCP Configuration Examples

stdio Transport (Recommended for local IDEs e.g., Cursor, Claude Desktop)

Configure your IDE's mcp.json to launch the MCP server via uvx:

{
  "mcpServers": {
    "plane-agent": {
      "command": "uvx",
      "args": [
        "--from",
        "plane-agent",
        "plane-mcp"
      ],
      "env": {
        "PLANE_BASE_URL": "your_plane_base_url_here",
        "PLANE_WORKSPACE_SLUG": "your_plane_workspace_slug_here",
        "DEBUG": "your_debug_here",
        "PYTHONUNBUFFERED": "your_pythonunbuffered_here",
        "PLANE_API_KEY": "your_plane_api_key_here"
      }
    }
  }
}

Streamable-HTTP Transport (Recommended for production deployments)

Configure your client's mcp.json to launch the Streamable-HTTP server via uvx with explicit host and port definition:

{
  "mcpServers": {
    "plane-agent": {
      "command": "uvx",
      "args": [
        "--from",
        "plane-agent",
        "plane-mcp"
      ],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "0.0.0.0",
        "PORT": "8000",
        "PLANE_BASE_URL": "your_plane_base_url_here",
        "PLANE_WORKSPACE_SLUG": "your_plane_workspace_slug_here",
        "DEBUG": "your_debug_here",
        "PYTHONUNBUFFERED": "your_pythonunbuffered_here",
        "PLANE_API_KEY": "your_plane_api_key_here"
      }
    }
  }
}

Alternatively, connect to a pre-deployed remote or local Streamable-HTTP instance:

{
  "mcpServers": {
    "plane-agent": {
      "url": "http://localhost:8000/plane-agent/mcp"
    }
  }
}

Deploying the Streamable-HTTP server via Docker:

docker run -d \
  --name plane-agent-mcp \
  -p 8000:8000 \
  -e TRANSPORT=streamable-http \
  -e PORT=8000 \
  -e PLANE_BASE_URL="your_value" \
  -e PLANE_WORKSPACE_SLUG="your_value" \
  -e DEBUG="your_value" \
  -e PYTHONUNBUFFERED="your_value" \
  -e PLANE_API_KEY="your_value" \
  knucklessg1/plane-agent:latest

Agent

This repository features a fully integrated Pydantic AI Graph Agent. It communicates over the Agent Control Protocol (ACP) and interacts seamlessly with the Agent Web UI (AG-UI) and Terminal interface.

Running the Agent CLI

To start the interactive command-line agent:

# Set credentials
export PLANE_BASE_URL="your_value"
export PLANE_WORKSPACE_SLUG="your_value"
export DEBUG="your_value"
export PYTHONUNBUFFERED="your_value"
export PLANE_API_KEY="your_value"

# Run the agent server
plane-agent --provider openai --model-id gpt-4o

Docker Compose Orchestration

The following docker/agent.compose.yml configures the Agent, Web UI, and Terminal Interface together:

version: '3.8'

services:
  plane-agent-mcp:
    image: knucklessg1/plane-agent:latest
    container_name: plane-agent-mcp
    hostname: plane-agent-mcp
    restart: always
    env_file:
      - ../.env
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=8000
      - TRANSPORT=streamable-http
    ports:
      - "8000:8000"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

  plane-agent-agent:
    image: knucklessg1/plane-agent:latest
    container_name: plane-agent-agent
    hostname: plane-agent-agent
    restart: always
    depends_on:
      - plane-agent-mcp
    env_file:
      - ../.env
    command: [ "plane-agent" ]
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=9004
      - MCP_URL=http://plane-agent-mcp:8000/mcp
      - PROVIDER=${PROVIDER:-openai}
      - MODEL_ID=${MODEL_ID:-gpt-4o}
      - ENABLE_WEB_UI=True
      - ENABLE_OTEL=True
    ports:
      - "9004:9004"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:9004/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

Detailed graph node architecture explanations, custom skill configurations, and agentic trace guides are available in docs/overview.md and docs/index.md.

Security & Governance

Built directly upon the enterprise-ready agent-utilities core, standard security parameters are fully supported:

Access Control & Policy Enforcement

Eunomia Policies: Fine-grained, policy-driven tool authorization. Supports none, local embedded (mcp_policies.json), or centralized remote modes.
OIDC Token Delegation: Compliant with RFC 8693 token exchange for flowing authenticating user credentials from Web UI / ACP → Agent → MCP.
Scoped Credentials: Execution context runs restricted to the specific caller identity.

Runtime Security Grid

Feature	Functionality	Enablement
Tool Guard	Sensitivity inspection with human-in-the-loop validation	Enabled by default
Prompt Injection Defense	Input scanning, repetition monitoring, and recursive loop blocks	Enabled by default
Context Safety Guard	Stuck-loop detectors and contextual overflow preemptive alerts	Enabled by default

Environment Variables

The Plane Agent supports the following environment variables for configuration and integration:

Variable	Description
`PLANE_BASE_URL`	The base URL of the Plane instance.
`PLANE_WORKSPACE_SLUG`	The workspace slug of the Plane workspace.
`PLANE_API_KEY`	The API key for authentication with Plane.
`MCP_URL`	The URL of the MCP server.
`MODEL_ID`	Default LLM model identifier (e.g. `gpt-4o`).
`PROVIDER`	The LLM provider (e.g. `openai`, `anthropic`).
`ENABLE_WEB_UI`	Set to `True` to enable the built-in Web UI.
`ENABLE_OTEL`	Set to `True` to enable OpenTelemetry telemetry.
`AGENT_UTILITIES_TESTING`	Set to `True` during testing to bypass production setups.
`AUTH_TYPE`	The authentication type to use (e.g., jwt, none).
`DEFAULT_API_KEY`	Default API key for fast server fallback authentication.
`OTEL_EXPORTER_OTLP_ENDPOINT`	The OpenTelemetry OTLP endpoint.
`PROJECTSTOOL`	Set to `True`/`False` to toggle the Projects tool module.
`WORK_ITEMSTOOL`	Set to `True`/`False` to toggle the Work Items tool module.
`CYCLESTOOL`	Set to `True`/`False` to toggle the Cycles tool module.
`EPICSTOOL`	Set to `True`/`False` to toggle the Epics tool module.
`MILESTONESTOOL`	Set to `True`/`False` to toggle the Milestones tool module.
`MODULESTOOL`	Set to `True`/`False` to toggle the Modules tool module.
`STATESTOOL`	Set to `True`/`False` to toggle the States tool module.
`USERSTOOL`	Set to `True`/`False` to toggle the Users tool module.
`WORKSPACESTOOL`	Set to `True`/`False` to toggle the Workspaces tool module.
`INITIATIVESTOOL`	Set to `True`/`False` to toggle the Initiatives tool module.
`INTAKETOOL`	Set to `True`/`False` to toggle the Intake tool module.
`LABELSTOOL`	Set to `True`/`False` to toggle the Labels tool module.
`PAGESTOOL`	Set to `True`/`False` to toggle the Pages tool module.

Installation

Install the Python package locally:

# Using uv (highly recommended)
uv pip install plane-agent[all]

# Using standard pip
python -m pip install plane-agent[all]

Repository Owners

Contribute

Contributions are welcome! Please ensure code quality by executing local checks before submitting pull requests:

Format code using ruff format .
Lint code using ruff check .
Validate type-safety with mypy .
Execute test suites using pytest

Name		Name	Last commit message	Last commit date
Latest commit History 106 Commits
.github/workflows		.github/workflows
.specify		.specify
docker		docker
docs		docs
plane_agent		plane_agent
scripts		scripts
tests		tests
.bumpversion.cfg		.bumpversion.cfg
.codespellignore		.codespellignore
.dockerignore		.dockerignore
.env.example		.env.example
.gitattributes		.gitattributes
.gitignore		.gitignore
.gitignore copy		.gitignore copy
.pre-commit-config.yaml		.pre-commit-config.yaml
AGENTS.md		AGENTS.md
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
LICENSE copy		LICENSE copy
MANIFEST.in		MANIFEST.in
README copy.md		README copy.md
README.md		README.md
a2a.json		a2a.json
mcp_config.json		mcp_config.json
mkdocs.yml		mkdocs.yml
opencode.json		opencode.json
pyproject.toml		pyproject.toml
pytest.ini		pytest.ini
requirements.txt		requirements.txt
uv.lock		uv.lock

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Plane Agent

CLI or API | MCP | Agent

Table of Contents

Overview

Key Features

CLI or API

MCP

Available MCP Tools

Dynamic Tool Selection & Visibility

MCP Configuration Examples

stdio Transport (Recommended for local IDEs e.g., Cursor, Claude Desktop)

Streamable-HTTP Transport (Recommended for production deployments)

Agent

Running the Agent CLI

Docker Compose Orchestration

Security & Governance

Access Control & Policy Enforcement

Runtime Security Grid

Environment Variables

Installation

Repository Owners

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Plane Agent

CLI or API | MCP | Agent

Table of Contents

Overview

Key Features

CLI or API

MCP

Available MCP Tools

Dynamic Tool Selection & Visibility

MCP Configuration Examples

stdio Transport (Recommended for local IDEs e.g., Cursor, Claude Desktop)

Streamable-HTTP Transport (Recommended for production deployments)

Agent

Running the Agent CLI

Docker Compose Orchestration

Security & Governance

Access Control & Policy Enforcement

Runtime Security Grid

Environment Variables

Installation

Repository Owners

Contribute

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