Codebuddy MCP Server
A lightweight Cognitive Scaffolding Platform that provides advanced task decomposition, metacognitive guidance, and intelligent memory for AI agents.
Built on PhD-level research in cognitive load theory, hierarchical task networks, and prompt engineering best practices.
🧠 Cognitive Features
Smart Task Planning: Hierarchical decomposition respecting Miller's 7±2 rule
Metacognitive Guidance: Self-reflection prompts and adaptive strategies
Complexity Assessment: Automatic cognitive load evaluation and management
Pattern Recognition: Learning from successful project structures
Software Engineering Integration: Clean Code and SOLID principle guidance
Tool Usage Nudges: Smart suggestions for AI agents to use complementary tools
🚀 Core Capabilities
Hierarchical Planning: Break complex problems using proven cognitive frameworks
Progress Tracking: Update status with learning capture and insight generation
Persistent Memory: Append-only JSONL storage with cognitive metadata
Intelligent Search: Context-aware task discovery with success pattern matching
Strategic Learning: Extract actionable insights from completed projects
Quick Start
Local Development
Docker
Docker Compose
MCP Tools
plan_task(problem: str)
- Create a new task with generated stepsupdate_task(task_id: str, status: str, notes: str)
- Update task progresslist_tasks(limit: int = 10)
- Get recent taskssearch_tasks(query: str)
- Find tasks by keywordsummarize_lessons()
- Analyze success patterns and blockers
Configuration
The server accepts the following command-line arguments:
--host
- Host address to bind to (default: localhost)--port
- Port number to bind to (default: 8000)--data-file
- Path to JSONL storage file (default: data/tasks.jsonl)--log-level
- Logging level (default: INFO)
Storage Format
Tasks are stored in data/tasks.jsonl
with one JSON object per line:
Architecture
The server follows Clean Code and SOLID principles:
models.py - Pydantic data models and validation
storage.py - JSONL persistence with cross-platform file locking
tools.py - MCP tool implementations and business logic
error_handling.py - Structured error handling and health monitoring
codebuddy.py - Main server application with FastMCP integration
This server cannot be installed
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
A cognitive scaffolding platform that helps AI agents break down complex tasks into manageable steps using hierarchical planning and metacognitive guidance. Provides persistent memory, progress tracking, and intelligent pattern recognition to learn from successful project structures.