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Codebuddy MCP Server

by jacklatrobe

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

pip install -r requirements.txt python codebuddy.py --host 0.0.0.0 --port 8000

Docker

docker build -t codebuddy-mcp . docker run -p 8000:8000 -v $(pwd)/data:/app/data codebuddy-mcp

Docker Compose

docker-compose up -d

MCP Tools

  • plan_task(problem: str) - Create a new task with generated steps

  • update_task(task_id: str, status: str, notes: str) - Update task progress

  • list_tasks(limit: int = 10) - Get recent tasks

  • search_tasks(query: str) - Find tasks by keyword

  • summarize_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:

{ "id": "uuid", "problem": "string", "steps": ["string"], "status": "planned|in_progress|completed|blocked", "notes": "string", "created_at": "iso8601", "updated_at": "iso8601" }

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

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

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.

  1. 🧠 Cognitive Features
    1. 🚀 Core Capabilities
      1. Quick Start
        1. Local Development
        2. Docker
        3. Docker Compose
      2. MCP Tools
        1. Configuration
          1. Storage Format
            1. Architecture

              MCP directory API

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