Task Manager MCP Server

AI‑powered task management system for AI agents. Maps tasks, picks daily work, tracks velocity, and integrates with agent‑modes session context.

Features

• Task board with backlog/ready/in‑progress/done columns

• Daily planning – pick tasks for today, log completions

• Velocity tracking – empirical tasks/day based on recent logs

• Forecasting – predict when backlog will be cleared

• Session integration – scan recent agent‑modes context notes for work signals

• Rule‑based AI recommendations – suggests daily tasks based on priority, dependencies, category balance, and recent completion

• DeepSeek AI integration – smarter recommendations, task parsing, session analysis, and summarization

Related MCP server: Project Manager MCP

Tools

Task Management

• create_task – add a new task (title, category, priority, etc.)

• get_task – retrieve a single task by ID

• list_tasks – list tasks with optional filtering (category, status, tags)

• update_task – modify any task field

• delete_task – remove a task

Daily Planning

• get_daily_log – get today’s (or any date’s) planned/completed tasks

• plan_day – add task IDs to today’s plan

• complete_tasks – mark tasks as done (updates daily log and task status)

• list_daily_logs – list logs within a date range

• get_daily_summary – generate completion rate and category breakdown

Projections

• get_projections – retrieve current velocity and forecast

• update_velocity – recalculate velocity from recent completions

• forecast_completion – estimate when backlog will be finished

Session Integration

• scan_recent_sessions – scan agent‑modes session context notes from the last N days

AI‑Assisted Planning

• recommend_daily_tasks – rule‑based recommendation for today’s tasks (priority, dependencies, category balance, recent history)

• recommend_daily_tasks_ai – DeepSeek-powered recommendations with sophisticated reasoning

• analyze_session_for_tasks – parse agent‑modes session notes to detect task completion and progress

• parse_natural_language_task – convert informal descriptions into structured tasks

• summarize_task_description – create concise, clear summaries of lengthy task descriptions

• generate_weekly_summary – AI‑written narrative summary of weekly productivity

Data Storage

All data is stored in .opencode/tasks/ as JSON files:

• tasks.json – all tasks

• daily‑logs.json – daily plans and completions

• projections.json – velocity and forecast

Integration with Agent‑Modes

The server reads .opencode/sessions/<id>/context.md files to detect work‑completion signals (future enhancement: auto‑update task status based on session notes).

Usage in OpenCode

Once registered in opencode.json, the tools are available to all agents (subject to agent‑level permissions). Example:

# Create a coding task
task_manager create_task title="Implement user auth" category=coding priority=1

# Plan today’s work
task_manager plan_day task_ids=["task-id-1","task-id-2"]

# Get recommendations (rule-based)
task_manager recommend_daily_tasks max_tasks=3 category=coding

# Get AI-powered recommendations
task_manager recommend_daily_tasks_ai max_tasks=3 use_ai_reasoning=true

# Parse natural language into structured task
task_manager parse_natural_language_task description="need to fix the login bug that happens on mobile safari"

# Analyze session notes for task completion
task_manager analyze_session_for_tasks session_id="20250304T123456Z_abc123"

# Generate weekly summary
task_manager generate_weekly_summary start_date="2025-03-01" end_date="2025-03-07"

# Mark tasks as done
task_manager complete_tasks task_ids=["task-id-1"]

# Check velocity
task_manager update_velocity days=7
task_manager forecast_completion

Configuration

The server is registered in opencode.json under the key task_manager. It runs as a local stdio MCP server.

DeepSeek API Key

For AI features, set the DEEPSEEK_API_KEY environment variable. The server includes a default key for testing, but for production use you should:

1. Get your own API key from DeepSeek Platform

2. Set it as environment variable or replace the default in llm‑bridge.mjs

export DEEPSEEK_API_KEY=your_key_here
# or on Windows:
set DEEPSEEK_API_KEY=your_key_here

Development

Files:

• server.mjs – MCP transport boilerplate

• core.mjs – tool definitions and execution router

• task‑store.mjs – persistence layer (JSON files)

• daily‑planner.mjs – rule‑based recommendation logic

• llm‑bridge.mjs – DeepSeek API integration for AI features

• README.md – this file

Next Steps (Planned)

✅ Implemented

1. LLM bridge – DeepSeek API integration for smarter recommendations, task parsing, and summarization

2. Session integration – scan agent‑modes context notes (manual analysis via analyze_session_for_tasks)

🔄 Remaining 3. Auto‑completion – automatically update task status based on session notes (future enhancement) 4. Custom OpenCode commands – e.g., /task‑board, /plan‑today 5. Initial seed – pre‑populate with example tasks from existing projects 6. Web UI – optional browser‑based task board visualization