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Backlog Manager MCP Server

by danielscholl
GitHub
Python
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Backlog Manager MCP Server

A simple task tracking and backlog management MCP server for AI assistants (hack project)

Table of Contents

Overview

Backlog Manager is an MCP (Machine-Consumable Programming) server for issue and task management with a file-based approach. It provides tools for AI agents and other clients to create issues, add tasks to them, and track task status. Issues represent high-level feature requests or bugs, while tasks represent specific work items needed to resolve the issue.

Built using Anthropic's MCP protocol, it supports both SSE and stdio transports for flexible integration with AI assistants like Claude, or other MCP-compatible clients.

Features

  • Issue Management: Create, list, select, and track issues with descriptions

  • Task Tracking: Add tasks to issues with titles, descriptions, and status tracking

  • Status Workflow: Track task progress through New, InWork, and Done states

  • File-Based Storage: Portable JSON storage format for easy backup and version control

  • Flexible Transport: Support for both SSE (HTTP) and stdio communication

  • Docker Support: Run in containers for easy deployment and isolation

Prerequisites

  • Python: 3.12 or higher

  • Package Manager: uv (recommended) or pip

  • Docker: (Optional) For containerized deployment

  • MCP Client: Claude Code, Windsurf, or any other MCP-compatible client

Installation

Using uv (Recommended)

# Clone the repository git clone https://github.com/username/backlog-manager-mcp.git cd backlog-manager-mcp # Install dependencies uv pip install -e . # Verify installation uv run backlog-manager # This should start the server

Using Docker

# Build the Docker image docker build -t backlog/manager --build-arg PORT=8050 . # Run the container docker run -p 8050:8050 backlog/manager # Verify container is running docker ps | grep backlog/manager

Configuration

Configure the server behavior using environment variables in a .env file:

# Create environment file from example cp .env.example .env

Example .env file content:

# Transport mode: 'sse' or 'stdio' TRANSPORT=sse # Server configuration (for SSE transport) HOST=0.0.0.0 PORT=8050 # Data storage TASKS_FILE=tasks.json

Variable

Description

Default

Required

TRANSPORT

Transport protocol (sse or stdio)

sse

No

HOST

Host to bind to when using SSE transport

0.0.0.0

No

PORT

Port to listen on when using SSE transport

8050

No

TASKS_FILE

Path to the tasks storage file

tasks.json

No

Running the Server

Start the Server (SSE Mode)

# Using the CLI command uv run backlog-manager # Or directly with Python uv run src/backlog_manager/main.py

You should see output similar to:

INFO: Started server process [12345] INFO: Waiting for application startup. INFO: Application startup complete. INFO: Uvicorn running on http://0.0.0.0:8050 (Press CTRL+C to quit)

Note: The server does not support the --help flag since it's designed as an MCP server, not a traditional CLI application.

Using stdio Mode

When using stdio mode, you don't need to start the server separately - the MCP client will start it automatically when configured properly (see Integration with MCP Clients).

MCP Tools

The Backlog Manager exposes the following tools via MCP:

Issue Management

Tool

Description

Parameters

create_issue

Create a new issue

name

(string),

description

(string, optional),

status

(string, optional)

list_issues

Show all available issues

None

select_issue

Set the active issue

name

(string)

initialize_issue

Create or reset an issue

name

(string),

description

(string, optional),

status

(string, optional)

update_issue_status

Update issue status

name

(string),

status

(string)

Task Management

Tool

Description

Parameters

add_task

Add task to active issue

title

(string),

description

(string, optional)

list_tasks

List tasks in active issue

status

(string, optional)

update_task_status

Update task status

task_id

(string),

status

(string)

Status Values

Tasks and issues can have one of the following statuses:

  • New (default for new tasks/issues)

  • InWork (in progress)

  • Done (completed)

Integration with MCP Clients

SSE Configuration

Once you have the server running with SSE transport, connect to it using this configuration:

{ "mcpServers": { "backlog-manager": { "transport": "sse", "url": "http://localhost:8050/sse" } } }

Windsurf Configuration:

{ "mcpServers": { "backlog-manager": { "transport": "sse", "serverUrl": "http://localhost:8050/sse" } } }

n8n Configuration:

Use host.docker.internal instead of localhost to access the host machine from n8n container:

http://host.docker.internal:8050/sse

Python with Stdio Configuration

{ "mcpServers": { "backlog-manager": { "command": "python", "args": ["path/to/backlog-manager/src/backlog_manager/main.py"], "env": { "TRANSPORT": "stdio", "TASKS_FILE": "tasks.json" } } } }

Docker with Stdio Configuration

{ "mcpServers": { "backlog-manager": { "command": "docker", "args": ["run", "--rm", "-i", "-e", "TRANSPORT=stdio", "backlog/manager"], "env": { "TRANSPORT": "stdio" } } } }

Example

Backlog Manager is designed to work seamlessly with AI assistants to help you organize your project work. The most powerful use case is having the AI read specifications and automatically create a structured backlog.

Simply ask your AI assistant:

Read the spec and create a backlog for features not completed.

The AI assistant will:

  1. Read and analyze the specification document

  2. Identify key features and components

  3. Create issues for main functional areas

  4. Break down each issue into specific tasks

  5. Organize everything in a structured backlog

This server cannot be installed

-
security - not tested
F
license - not found
-
quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A task tracking and backlog management tool that enables AI assistants to create, organize, and track issues and tasks with status workflow through MCP protocol.

  1. Table of Contents
    1. Overview
      1. Features
        1. Prerequisites
          1. Installation
            1. Using uv (Recommended)
            2. Using Docker
          2. Configuration
            1. Running the Server
              1. Start the Server (SSE Mode)
              2. Using stdio Mode
            2. MCP Tools
              1. Issue Management
              2. Task Management
              3. Status Values
            3. Integration with MCP Clients
              1. SSE Configuration
              2. Python with Stdio Configuration
              3. Docker with Stdio Configuration
            4. Example

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