mcp-code-understanding

mcp-code-understanding

1
developer_toolsknowledge_and_memory

The Code Understanding MCP Server is designed to comprehend codebases to aid AI coding assistants. It analyzes local and remote repositories to offer detailed insights and facilitate improved software development assistance.

⚠️ Platform Support Notice

This MCP server currently only works on Mac or Linux. Windows is NOT supported at this time.

Support for Windows is planned for a future release, and work is ongoing to enable compatibility.

Code Understanding MCP Server

An MCP (Model Context Protocol) server designed to understand codebases and provide intelligent context to AI coding assistants. This server handles both local and remote GitHub repositories and supports standard MCP-compliant operations.

Features

  • Clone and analyze GitHub repositories or local codebases
  • Get repository structure and file organization
  • Identify critical files based on complexity metrics and code structure
  • Generate detailed repository maps showing:
    • Function signatures and relationships
    • Class definitions and hierarchies
    • Code structure and dependencies
  • Retrieve and analyze repository documentation
  • Target analysis to specific files or directories
  • Keep analysis up-to-date with repository changes via refresh

Quick Start: MCP Client Configuration

Prerequisite: uv Installation

This server is launched using uvx, which is part of the uv Python package manager. If you don't already have uv installed, please install it first. You can find installation instructions in the "For Developers" section under "Prerequisites" below, or visit the official uv installation guide astral.sh/uv.

To use this server with your MCP client, add the following configuration to your MCP client's configuration file:

{
  "mcpServers": {
    "code-understanding": {
      "command": "uvx",
      "args": [
        "code-understanding-mcp-server"
      ]
    }
  }
}

That's it! The server will be automatically downloaded and run when needed by your MCP client.

Why Use this MCP Server?

MCP Code Understanding Server

Value Proposition

The MCP Code Understanding Server empowers AI assistants with comprehensive code comprehension capabilities, enabling them to provide more accurate, contextual, and practical assistance with software development tasks. By creating a semantic bridge between repositories and AI systems, this server dramatically reduces the time and friction involved in code exploration, analysis, and implementation guidance.

Common Use Cases

Reference Repository Analysis

  • Examine external repositories (libraries, dependencies, etc.) to inform current development
  • Find implementation patterns and examples in open-source projects
  • Understand how specific libraries work internally when documentation is insufficient
  • Compare implementation approaches across similar projects
  • Identify best practices from high-quality codebases

Knowledge Extraction and Documentation

  • Generate comprehensive documentation for poorly documented codebases
  • Create architectural overviews and component relationship diagrams
  • Develop progressive learning paths for developer onboarding
  • Extract business logic and domain knowledge embedded in code
  • Identify and document system integration points and dependencies

Codebase Assessment and Improvement

  • Analyze technical debt and prioritize refactoring efforts
  • Identify security vulnerabilities and compliance issues
  • Assess test coverage and quality
  • Detect dead code, duplicated logic, and optimization opportunities
  • Evaluate implementation against design patterns and architectural principles

Legacy System Understanding

  • Recover knowledge from systems with minimal documentation
  • Support migration planning by understanding system boundaries
  • Analyze complex dependencies before making changes
  • Trace feature implementations across multiple components
  • Understand historical design decisions and their rationales

Cross-Project Knowledge Transfer

  • Apply patterns from one project to another
  • Bridge knowledge gaps between teams working on related systems
  • Identify reusable components across multiple projects
  • Understand differences in implementation approaches between teams
  • Facilitate knowledge sharing in distributed development environments

For detailed examples of how the MCP Code Understanding Server can be used in real-world scenarios, see our document. It includes step-by-step walkthroughs of:

  • Accelerating developer onboarding to a complex codebase
  • Planning and executing API migrations
  • Conducting security vulnerability assessments

How It Works

The MCP Code Understanding Server processes repositories through a series of analysis steps:

  1. Repository Cloning: The server clones the target repository into its cache
  2. Structure Analysis: Analysis of directories, files, and their organization
  3. Critical File Identification: Determination of structurally significant components
  4. Documentation Retrieval: Collection of all documentation files
  5. Semantic Mapping: Creation of a detailed map showing relationships between components
  6. Content Analysis: Examination of specific files as needed for deeper understanding

AI assistants integrate with the server by making targeted requests for each analytical stage, building a comprehensive understanding of the codebase that can be used to address specific user questions and needs.

Design Considerations for Large Codebases

The server employs several strategies to maintain performance and usability even with enterprise-scale repositories:

  • Asynchronous Processing: Repository cloning and analysis occur in background threads, providing immediate feedback while deeper analysis continues
  • Progressive Analysis: Initial quick analysis enables immediate interaction, with more detailed understanding building over time
  • Scope Control: Parameters for max_tokens, files, and directories enable targeted analysis of specific areas of interest
  • Threshold Management: Automatic detection of repository size with appropriate guidance for analysis strategies
  • Hierarchical Understanding: Repository structure is analyzed first, enabling intelligent prioritization of critical components for deeper semantic analysis

These design choices ensure that developers can start working immediately with large codebases while the system builds a progressively deeper understanding in the background, striking an optimal balance between analysis depth and responsiveness.

GitHub Authentication (Optional)

If you need to access private repositories or want to avoid GitHub API rate limits, add your GitHub token to the configuration:

{
  "mcpServers": {
    "code-understanding": {
      "command": "uvx",
      "args": [
        "code-understanding-mcp-server"
      ],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "your-token-here"
      }
    }
  }
}

Advanced Configuration Options

For advanced users, the server supports several configuration options:

{
  "mcpServers": {
    "code-understanding": {
      "command": "uvx",
      "args": [
        "code-understanding-mcp-server",
        "--cache-dir", "~/custom-cache-dir",     // Override repository cache location
        "--max-cached-repos", "20",              // Override maximum number of cached repos
        "--transport", "stdio",                  // Transport type (stdio or sse)
        "--port", "3001"                         // Port for SSE transport (only used with sse)
      ],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "your-token-here"
      }
    }
  }
}

Available options:

  • --cache-dir: Override the repository cache directory location (default: ~/.cache/mcp-code-understanding)
  • --max-cached-repos: Set maximum number of cached repositories (default: 10)
  • --transport: Choose transport type (stdio or sse, default: stdio)
  • --port: Set port for SSE transport (default: 3001, only used with sse transport)

Server Configuration

The server uses a config.yaml file for base configuration. This file is automatically created in the standard configuration directory (~/.config/mcp-code-understanding/config.yaml) when the server first runs. You can also place a config.yaml file in your current directory to override the default configuration.

Here's the default configuration structure:

name: "Code Understanding Server"
log_level: "debug"

repository:
  cache_dir: "~/.cache/mcp-code-understanding"
  max_cached_repos: 10

documentation:
  include_tags:
    - markdown
    - rst
    - adoc
  include_extensions:
    - .md
    - .markdown
    - .rst
    - .txt
    - .adoc
    - .ipynb
  format_mapping:
    tag:markdown: markdown
    tag:rst: restructuredtext
    tag:adoc: asciidoc
    ext:.md: markdown
    ext:.markdown: markdown
    ext:.rst: restructuredtext
    ext:.txt: plaintext
    ext:.adoc: asciidoc
    ext:.ipynb: jupyter
  category_patterns:
    readme: 
      - readme
    api: 
      - api
    documentation:
      - docs
      - documentation
    examples:
      - examples
      - sample

For Developers

Prerequisites

  • Python 3.11 or 3.12: Required for both development and usage 
    # Verify your Python version
python --version
# or
python3 --version
  • UV Package Manager: The modern Python package installer 
    # Install UV
curl -sSf https://astral.sh/uv/install.sh | sh

Development Setup

To contribute or run this project locally:

# 1. Clone the repository
git clone https://github.com/yourusername/mcp-code-understanding.git
cd mcp-code-understanding

# 2. Create virtual environment
uv venv

# 3. Activate the virtual environment
#    Choose the command appropriate for your operating system and shell:

#    Linux/macOS (bash/zsh):
source .venv/bin/activate

#    Windows (Command Prompt - cmd.exe):
.venv\\Scripts\\activate.bat

#    Windows (PowerShell):
#    Note: You might need to adjust your execution policy first.
#    Run: Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope Process
.venv\\Scripts\\Activate.ps1

# 4. Install dependencies (editable mode with dev extras)
#    (Ensure your virtual environment is activated first!)
uv pip install -e ".[dev]"

# 5. Set up pre-commit hooks
pre-commit install

# 6. Run tests
uv run pytest

# 7. Test the server using MCP inspector
# Without GitHub authentication:
uv run mcp dev src/code_understanding/mcp/server/app.py

# With GitHub authentication (for testing private repos):
GITHUB_PERSONAL_ACCESS_TOKEN=your_token_here uv run mcp dev src/code_understanding/mcp/server/app.py

This will launch an interactive console where you can test all MCP server endpoints directly.

Development Tools

The following development tools are available after installing with dev extras (.[dev]):

Run tests with coverage:

uv run pytest

Format code (using black and isort):

# Format with black
uv run black .

# Sort imports
uv run isort .

Type checking with mypy:

uv run mypy .

All tools are configured via pyproject.toml with settings optimized for this project.

Publishing to PyPI

When you're ready to publish a new version to PyPI, follow these steps:

  1. Update the version number in pyproject.toml:

    # Edit pyproject.toml and change the version field
# For example: version = "0.1.1"

  2. Clean previous build artifacts:

    # Remove previous distribution packages and build directories
rm -rf dist/ 2>/dev/null || true
rm -rf build/ 2>/dev/null || true
rm -rf src/*.egg-info/ 2>/dev/null || true

  3. Build the distribution packages:

    uv run python -m build

  4. Verify the built packages:

    ls dist/

  5. Upload to PyPI (use TestPyPI first if unsure):

    # Install twine if you haven't already
uv pip install twine

# For PyPI release:
uv run python -m twine upload dist/*

You'll need PyPI credentials configured or you'll be prompted to enter them during upload.

License

MIT