MCP Server for Microsoft Planner

An unofficial MCP server that connects AI assistants to Microsoft Planner. Ask your AI assistant to create tasks, organise plans, manage buckets, and more, all through natural language.

This is an unofficial, community/open-source MCP server for Microsoft Planner.
It is not affiliated with, endorsed by, or sponsored by Microsoft.

Built with FastMCP and authenticated via Microsoft Entra ID (Azure AD) using the On-Behalf-Of (OBO) flow to call Microsoft Graph.

Table of Contents

• What Can It Do?
• Prerequisites
• Azure Entra ID Setup
• Installation
• Configuration
• Running the Server
• Connecting an MCP Client
• Available Tools
• Development
• Contributing
• License
• Security

What Can It Do?

Once connected, you can ask your AI assistant things like:

• "Show me all my Planner tasks that are overdue"
• "Create a task called 'Prepare Q3 report' in the Marketing plan"
• "Move all incomplete tasks in the Sprint bucket to the Backlog bucket"
• "Mark the 'Update docs' task as complete"

The AI assistant translates your request into the appropriate tool calls automatically.

Note: This MCP only supports Planner basic tasks and plans

Prerequisites

• A Microsoft 365 account with access to Microsoft Planner
• An Azure Entra ID (Azure AD) app registration (see Azure Setup below)
• An MCP-compatible client — for example:
  • VS Code
  • Claude Desktop

To run the server locally you also need:

• Python 3.12+
• uv (Python package manager)

Or, to run via Docker:

• Docker

Azure Entra ID Setup

An Azure app registration is required so the server can authenticate users and call Microsoft Graph on their behalf. You need admin access to an Azure Entra ID tenant (or ask your IT administrator).

Step 1 — Register the Application

1. Go to the Azure Portal → Microsoft Entra ID → App registrations → New registration
2. Enter a name (e.g. Microsoft Planner MCP)
3. Under Supported account types, choose the option appropriate for your organisation
4. Set the Redirect URI to Web → http://localhost:8000/auth/callback
5. Click Register

Step 2 — Configure API Permissions

1. In your new app registration, go to API permissions → Add a permission → Microsoft Graph → Delegated permissions
2. Add these permissions:
   • Tasks.ReadWrite — read and write Planner tasks
   • User.Read — read the signed-in user's profile
3. Click Grant admin consent for your organisation

Step 3 — Expose an API Scope

1. Go to Expose an API
2. Set the Application ID URI (accept the default api://<client-id> or customise it)
3. Click Add a scope:
   • Scope name: mcp-access
   • Who can consent: Admins and users (or Admins only if you prefer)
   • Fill in the display name and description
   • Set state to Enabled

Step 4 — Set Token Version

1. Go to Manifest (or Authentication → Advanced settings in newer portal versions)
2. Set "requestedAccessTokenVersion" to 2
3. Save

Step 5 — Create a Client Secret

1. Go to Certificates & secrets → New client secret
2. Add a description and choose an expiry period
3. Copy the Value immediately (it is only shown once)

Step 6 — Note Your IDs

You will need these three values for configuration:

Value	Where to Find It
Application (client) ID	App registration → Overview
Directory (tenant) ID	App registration → Overview
Client secret	The value copied in Step 5

Installation

Option A — Local (Python + uv)

git clone https://github.com/aixolotl/microsoft-planner-mcp
cd microsoft-planner-mcp
uv sync

Option B — Docker

No Python installation needed. See Running with Docker below.

Configuration

Copy the example environment file and fill in your Azure credentials:

cp .env.example .env

Edit .env:

# Azure App Registration (required)
CLIENT_ID=your-app-client-id
CLIENT_SECRET=your-app-client-secret
TENANT_ID=your-azure-tenant-id

# Public URL of this server (used for OAuth redirect URI)
BASE_URL=http://localhost:8000

# JSON-encoded list of allowed CORS origins
# Include http://localhost:6274 if using MCP Inspector for testing
ALLOWED_ORIGINS=["http://localhost:8000","http://localhost:6274"]

Running the Server

Local

uv run uvicorn src.server:app --host 0.0.0.0 --port 8000

The MCP endpoint is available at http://localhost:8000/mcp. A health check endpoint is at http://localhost:8000/health.

Running with Docker

# Pull the latest image
docker pull ghcr.io/aixolotl/microsoft-planner-mcp:latest

# Run with environment variables
docker run --rm -i \
  -e BASE_URL=https://localhost:8000 \
  -e CLIENT_ID=your_client_id \
  -e CLIENT_SECRET=your_api_token \
  -e TENANT_ID=your_tenant_id \
  -e ALLOWED_ORIGINS=["http://localhost:8000","http://localhost:6274", "http://localhost:3000"] \
  ghcr.io/aixolotl/microsoft-planner-mcp:latest

This starts the MCP server on port 8000.

Running with Docker compose

docker compose up

This starts the MCP server on port 8000. The Docker Compose configuration also includes a Jaeger instance for trace visualisation (see OpenTelemetry Tracing).

Connecting an MCP Client

Once the server is running, configure your MCP client to connect to it.

VS Code

Add the following to your VS Code settings (.vscode/settings.json in your project, or your user settings):

{
  "mcp": {
    "servers": {
      "planner": {
        "type": "http",
        "url": "http://localhost:8000/mcp"
      }
    }
  }
}

Then use Copilot Chat in Agent mode and ask it to interact with your Planner tasks. Copilot will discover the available tools automatically.

Other MCP Clients

Any client that supports the Streamable HTTP transport can connect by pointing to http://localhost:8000/mcp. The server advertises OAuth metadata automatically — the client handles the authentication flow.

Available Tools

All tools are available to your AI assistant automatically once connected. You don't need to call them directly — just describe what you want in natural language. The parameter details below are provided for reference and for client developers.

Read-only tools are annotated with readOnlyHint: true so clients can skip confirmation prompts. Destructive tools (deletes) are annotated with destructiveHint: true.

User

get_me

Return the authenticated user's profile from Microsoft Graph.

• Parameters: None
• Returns: User profile object (id, displayName, mail, etc.) or null

Groups

list_my_groups

List all Microsoft 365 groups the authenticated user is a member of.

Parameter	Type	Required	Description
select	string	No	Comma-separated fields to include (default: id,displayName,mail). Pass *all for all fields.
filter	string	No	OData filter expression, e.g. startsWith(displayName,'Project')
search	string	No	OData search string, e.g. "displayName:Project"

• Returns: List of group objects or null

Note: The Groups tool will not return details like name or mail of groups with the standard permissions Tasks.ReadWrite documented here, however searching and filtering still works, so you can find a group with a specific name using this tool.

Plans

list_my_plans

List Planner plans shared with the authenticated user.

Parameter	Type	Required	Description
select	string	No	Comma-separated fields to include (default: id,title,owner,createdBy,createdDateTime). Pass *all for all fields.

• Returns: List of plan objects or null

list_group_plans

List all Planner plans belonging to a Microsoft 365 group.

Parameter	Type	Required	Description
group_id	string	Yes	The object ID of the group (from list_my_groups)
select	string	No	Comma-separated fields to include (default: id,title,owner,createdBy,createdDateTime). Pass *all for all fields.

• Returns: List of plan objects or null

create_plan

Create a new Planner plan for a Microsoft 365 group.

Parameter	Type	Required	Description
group_id	string	Yes	The object ID of the M365 group that will own the plan
title	string	Yes	Display title for the new plan

• Returns: The created plan object or null

delete_plan

Delete a Planner plan.

Parameter	Type	Required	Description
plan_id	string	Yes	The ID of the plan to delete
etag	string	Yes	The current @odata.etag of the plan (retries once if stale)

• Returns: Confirmation message

list_plan_categories

Get category label definitions for a Planner plan. Returns all 25 category slots with their key (e.g. category1) and display name.

Parameter	Type	Required	Description
plan_id	string	Yes	The ID of the plan

• Returns: List of category objects (key, display_name) or null

Note: The list of categories is static as the API does not return display names or colours. But this endpoint awaits Microsoft's future updates to the API to fully support categories.

Buckets

list_buckets

List all buckets in a Planner plan.

Parameter	Type	Required	Description
plan_id	string	Yes	The ID of the plan

• Returns: List of bucket objects or null

create_bucket

Create a new bucket in a Planner plan.

Parameter	Type	Required	Description
plan_id	string	Yes	The ID of the plan to create the bucket in
name	string	Yes	Display name for the new bucket

• Returns: The created bucket object or null

delete_bucket

Delete a Planner bucket.

Parameter	Type	Required	Description
bucket_id	string	Yes	The ID of the bucket to delete
etag	string	Yes	The current @odata.etag of the bucket (retries once if stale)

• Returns: Confirmation message

Tasks

list_my_tasks

List all Planner tasks assigned to the authenticated user across all plans.

Parameter	Type	Required	Description
select	string	No	Comma-separated fields to include (default: *all). Pass *all for all fields.
filter	string	No	OData filter expression, e.g. percentComplete ne 100
search	string	No	Free-text search matched against the task title

• Returns: List of task objects or null

list_tasks

List all tasks in a Planner plan.

Parameter	Type	Required	Description
plan_id	string	Yes	The ID of the plan
select	string	No	Comma-separated fields to include (default: *all). Pass *all for all fields.
filter	string	No	OData filter expression, e.g. percentComplete eq 0
search	string	No	Free-text search matched against the task title

• Returns: List of task objects or null

get_task_details

Get the full details for a task: description, checklist items, and external references.

Parameter	Type	Required	Description
task_id	string	Yes	The ID of the task

• Returns: Task details object (description, checklist, references) or null

create_task

Create a new task in a Planner plan.

Parameter	Type	Required	Description
plan_id	string	Yes	The ID of the plan
bucket_id	string	Yes	The ID of the bucket to place the task in
title	string	Yes	Title of the task
start_date_time	string	No	ISO 8601 start date (e.g. 2026-05-01T00:00:00)
due_date_time	string	No	ISO 8601 due date (e.g. 2026-05-31T00:00:00)
percent_complete	integer	No	Completion percentage, 0–100
assign_user_ids	list[string]	No	User object IDs to assign to the task

• Returns: The created task object or null

update_task

Update a task's basic fields. Only provided fields are changed.

Parameter	Type	Required	Description
task_id	string	Yes	The ID of the task
etag	string	Yes	The current @odata.etag of the task (retries once if stale)
title	string	No	New title
percent_complete	integer	No	Completion percentage, 0–100
due_date_time	string	No	ISO 8601 due date
bucket_id	string	No	ID of the bucket to move the task to
assignee_priority	string	No	Order hint for sorting within the assignee's task list
assign_user_ids	list[string]	No	User IDs to assign
unassign_user_ids	list[string]	No	User IDs to remove

• Returns: The updated task object or null

update_task_details

Update a task's description, checklist items, and external references.

Parameter	Type	Required	Description
task_id	string	Yes	The ID of the task
etag	string	Yes	The current @odata.etag of the task details resource (retries once if stale)
description	string	No	Plain-text description (up to 2000 characters)
preview_type	string	No	Preview style: automatic, noPreview, checklist, description, or reference
checklist_items	object	No	Dict keyed by checklist item GUID. Pass null for a key to delete that item.
references	object	No	Dict keyed by URL-encoded reference URL. Pass null for a key to delete that reference.

• Returns: The updated task details object or null

list_task_fields

Return metadata for every field on a Planner task, including its data type, description, whether it is writable, and whether it requires a separate get_task_details call.

• Parameters: None
• Returns: List of field metadata objects (name, type, description, writable, detailed)

Note: The list of fields is static as Planner Standard doesn't support custom fields. But this endpoint awaits Microsoft's future updates to the API to fully support Planner Premium.

delete_task

Delete a Planner task.

Parameter	Type	Required	Description
task_id	string	Yes	The ID of the task
etag	string	Yes	The current @odata.etag of the task (retries once if stale)

• Returns: Confirmation message

Development

This section covers building, testing, and contributing to the project.

Project Structure

src/
├── server.py                  # FastMCP app, middleware, route mounting
├── config.py                  # Settings via pydantic-settings
├── auth_provider.py           # Azure OAuth provider (OBO flow)
├── deps.py                    # Shared dependency helpers
├── graph_client_manager.py    # Singleton GraphClientManager with per-user OBO clients
├── telemetry.py               # OpenTelemetry setup
├── types.py                   # Shared structural types
├── services/
│   └── planner_service.py     # Business logic wrapping Graph SDK calls
└── tools/
    ├── me.py                  # get_me
    ├── groups.py              # list_my_groups
    ├── plans.py               # plan tools + list_plan_categories
    ├── tasks.py               # task tools + list_task_fields
    └── buckets.py             # bucket tools
tests/
├── conftest.py
├── test_buckets_tool.py
├── test_groups_tool.py
├── test_planner_service.py
├── test_plans_tool.py
└── test_tasks_tool.py

Installing Dev Dependencies

uv sync --dev

Running Tests

uv run pytest

With verbose output:

uv run pytest -v

Architecture

The server is built with FastMCP and uses these key patterns:

Server composition — Tools are split into five domain routers (me, groups, plans, tasks, buckets), each a standalone FastMCP instance mounted on the main app. This keeps each domain's tools, imports, and tests isolated.

Authentication — The server uses FastMCP's OAuthProxy pattern via a custom AzureProvider. Azure Entra ID does not support Dynamic Client Registration (DCR), so the provider acts as a DCR-compliant proxy facing MCP clients while using the pre-registered app credentials with Azure. When a tool call arrives, the server exchanges the MCP session token for a Microsoft Graph token via the On-Behalf-Of flow, scoped to Tasks.ReadWrite and User.Read.

Middleware — Five built-in middleware layers are stacked on the server (outermost first):

Middleware	Purpose
ErrorHandlingMiddleware	Catches unhandled exceptions, logs full traces server-side, returns clean MCP errors
SlidingWindowRateLimitingMiddleware	60 req/min per client to protect the Microsoft Graph quota
TimingMiddleware	Records wall-clock duration for every MCP operation
StructuredLoggingMiddleware	Emits one JSON log line per request (method, status, duration, client info)
ResponseLimitingMiddleware	Truncates tool responses above 500 KB to prevent overflowing LLM context windows

Client logging — Every tool sends real-time progress messages to the MCP client via get_optional_context(), so users see status updates like "Fetching tasks…" and "Found 12 task(s)" in their client.

OpenTelemetry Tracing

The server includes native OpenTelemetry instrumentation with zero overhead when unused. To enable trace export:

# Install the optional OTEL dependency group
uv sync --group otel

# Set the OTLP endpoint and start the server
export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317
export OTEL_SERVICE_NAME=microsoft-planner-mcp
uv run uvicorn src.server:app --host 0.0.0.0 --port 8000

Traces follow the MCP semantic conventions and work with any OTLP-compatible backend (Jaeger, Grafana Tempo, Datadog, New Relic, etc.). The Docker Compose setup includes Jaeger with a UI at http://localhost:16686.

Testing with MCP Inspector

MCP Inspector lets you test tools interactively in a browser:

1. Start the server:

   uv run uvicorn src.server:app --host 0.0.0.0 --port 8000
   

2. In a separate terminal:

   npx @modelcontextprotocol/inspector
   

3. Open the URL printed in the terminal (e.g. http://localhost:6274/?MCP_PROXY_AUTH_TOKEN=...)

4. Set Transport Type to Streamable HTTP and URL to http://localhost:8000/mcp, then click Connect.

Contributing

Contributions are welcome! Please open an issue or submit a pull request. See the issue templates for bug reports, feature requests, and tasks.

License

This project is licensed under the MIT License.