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CodeGraphContext

Build Status PyPI version PyPI downloads GitHub stars License

An MCP server that indexes local code into a graph database to provide context to AI assistants.

Project Details

Features

  • Code Indexing: Analyzes Python code and builds a knowledge graph of its components.
  • Relationship Analysis: Query for callers, callees, class hierarchies, and more.
  • Live Updates: Watches local files for changes and automatically updates the graph.
  • Interactive Setup: A user-friendly command-line wizard for easy setup.

Used By

CodeGraphContext is already being explored by developers and projects for:

  • Static code analysis in AI assistants
  • Graph-based visualization of Python projects
  • Dead code and complexity detection

If you’re using CodeGraphContext in your project, feel free to open a PR and add it here! 🚀

Dependencies

  • neo4j>=5.15.0
  • watchdog>=3.0.0
  • requests>=2.31.0
  • stdlibs>=2023.11.18
  • typer[all]>=0.9.0
  • rich>=13.7.0
  • inquirerpy>=0.3.4
  • python-dotenv>=1.0.0

Getting Started

  1. Install: pip install codegraphcontext
  2. Setup: cgc setup
  3. Start: cgc start
  4. Index Code: cgc tool add-code-to-graph '{"path": "/path/to/your/project"}' (Under active development)

MCP Client Configuration

Add the following to your MCP client's configuration:

{
  "mcpServers": {
    "CodeGraphContext": {
      "command": "cgc",
      "args": [
        "start"
      ],
      "env": {
        "NEO4J_URI": "************",
        "NEO4J_USER": "************",
        "NEO4J_PASSWORD": "**************"
      },
      "tools": {
        "alwaysAllow": [
          "list_imports",
          "add_code_to_graph",
          "add_package_to_graph",
          "check_job_status",
          "list_jobs",
          "find_code",
          "analyze_code_relationships",
          "watch_directory",
          "find_dead_code",
          "execute_cypher_query",
          "calculate_cyclomatic_complexity",
          "find_most_complex_functions",
          "list_indexed_repositories",
          "delete_repository"
        ],
        "disabled": false
      },
      "disabled": false,
      "alwaysAllow": []
    }
  }
}

Natural Language Interaction Examples

Once the server is running, you can interact with it through your AI assistant using plain English. Here are some examples of what you can say:

Indexing and Watching Files

  • To index a new project:

    • "Please index the code in the /path/to/my-project directory." OR
    • "Add the project at ~/dev/my-other-project to the code graph."
  • To start watching a directory for live changes:

    • "Watch the /path/to/my-active-project directory for changes." OR
    • "Keep the code graph updated for the project I'm working on at ~/dev/main-app."

    When you ask to watch a directory, the system performs two actions at once:

    1. It kicks off a full scan to index all the code in that directory. This process runs in the background, and you'll receive a job_id to track its progress.
    2. It begins watching the directory for any file changes to keep the graph updated in real-time.

    This means you can start by simply telling the system to watch a directory, and it will handle both the initial indexing and the continuous updates automatically.

Querying and Understanding Code

  • Finding where code is defined:

    • "Where is the process_payment function?"
    • "Find the User class for me."
    • "Show me any code related to 'database connection'."
  • Analyzing relationships and impact:

    • "What other functions call the get_user_by_id function?"
    • "If I change the calculate_tax function, what other parts of the code will be affected?"
    • "Show me the inheritance hierarchy for the BaseController class."
    • "What methods does the Order class have?"
  • Exploring dependencies:

    • "Which files import the requests library?"
    • "Find all implementations of the render method."
  • Advanced Call Chain and Dependency Tracking (Spanning Hundreds of Files): The CodeGraphContext excels at tracing complex execution flows and dependencies across vast codebases. Leveraging the power of graph databases, it can identify direct and indirect callers and callees, even when a function is called through multiple layers of abstraction or across numerous files. This is invaluable for:

    • Impact Analysis: Understand the full ripple effect of a change to a core function.

    • Debugging: Trace the path of execution from an entry point to a specific bug.

    • Code Comprehension: Grasp how different parts of a large system interact.

    • "Show me the full call chain from the main function to process_data."

    • "Find all functions that directly or indirectly call validate_input."

    • "What are all the functions that initialize_system eventually calls?"

    • "Trace the dependencies of the DatabaseManager module."

  • Code Quality and Maintenance:

    • "Is there any dead or unused code in this project?"
    • "Calculate the cyclomatic complexity of the process_data function in src/utils.py."
    • "Find the 5 most complex functions in the codebase."
  • Repository Management:

    • "List all currently indexed repositories."
    • "Delete the indexed repository at /path/to/old-project."

Contributing

Contributions are welcome! 🎉
If you have ideas for new features, integrations, or improvements, open an issue or submit a PR.

Join discussions and help shape the future of CodeGraphContext.

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An MCP server that indexes local code into a graph database to provide context to AI assistants.

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