Persistent-Code MCP Server

Metadata-Version: 2.4 Name: persistent-code Version: 0.1.0 Summary: An MCP server for maintaining code knowledge across LLM chat sessions Home-page: https://github.com/yourusername/persistent-code-mcp Author: Your Name Author-email: your.email@example.com Classifier: Development Status :: 3 - Alpha Classifier: Intended Audience :: Developers Classifier: License :: OSI Approved :: MIT License Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3.10 Classifier: Programming Language :: Python :: 3.11 Requires-Python: >=3.10 Description-Content-Type: text/markdown Requires-Dist: mcp>=1.2.0 Requires-Dist: llama-index>=0.9.0 Requires-Dist: networkx>=3.1 Requires-Dist: sentence-transformers>=2.2.0 Requires-Dist: pydantic>=2.0.0 Requires-Dist: sqlalchemy>=2.0.0 Requires-Dist: fastapi>=0.103.0 Requires-Dist: uvicorn>=0.23.0 Requires-Dist: python-dotenv>=1.0.0 Dynamic: author Dynamic: author-email Dynamic: classifier Dynamic: description Dynamic: description-content-type Dynamic: home-page Dynamic: requires-dist Dynamic: requires-python Dynamic: summary # Persistent-Code MCP Server A Model Context Protocol (MCP) server that creates and maintains a knowledge graph of code generated by Claude. This allows maintaining context across sessions without requiring the entire codebase to be present in the context window. ## Problem & Solution When developing software with Claude: - Context windows are limited, making it difficult to work with large codebases - Previous code context is lost between sessions - Claude lacks persistent understanding of project structure - Redundant explanation of code is required in each session - Maintaining implementation consistency is challenging Persistent-Code solves these problems by: - Creating a knowledge graph of code components and their relationships - Tracking implementation status of each component - Providing tools to navigate, query, and understand the codebase - Assembling minimal necessary context for specific coding tasks - Maintaining persistent knowledge across chat sessions ## Installation ### Prerequisites - Python 3.10 or higher - UV package manager (recommended) or pip ### Setting Up ```bash # Clone repository git clone https://github.com/your-username/persistent-code-mcp.git cd persistent-code-mcp # Set up environment with UV uv venv source .venv/bin/activate # On Windows: .venv\Scripts\activate uv pip install -r requirements.txt # Or with pip python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install -r requirements.txt ``` ## Usage ### Initializing a Project ```bash python -m persistent_code init --project-name "YourProject" ``` ### Starting the Server ```bash python -m persistent_code serve --project-name "YourProject" ``` ### Configuring Claude for Desktop 1. Edit your Claude for Desktop config file: - Location: `~/Library/Application Support/Claude/claude_desktop_config.json` - Add the following configuration: ```json { "mcpServers": { "persistent-code": { "command": "python", "args": [ "-m", "persistent_code", "serve", "--project-name", "YourProject" ], "cwd": "/absolute/path/to/persistent-code-mcp" } } } ``` 2. Restart Claude for Desktop 3. Connect to your MCP server by asking Claude about your code ## Available Tools ### Knowledge Graph Management - `add_component`: Add a new code component to the graph - `update_component`: Update an existing component - `add_relationship`: Create a relationship between components ### Code Retrieval and Navigation - `get_component`: Retrieve a component by ID or name - `find_related_components`: Find components related to a given component - `search_code`: Search the codebase semantically ### Status Management - `update_status`: Update implementation status of a component - `get_project_status`: Retrieve implementation status across the project - `find_next_tasks`: Suggest logical next components to implement ### Context Assembly - `prepare_context`: Assemble minimal context for a specific task - `continue_implementation`: Provide context to continue implementing a component - `get_implementation_plan`: Generate a plan for implementing pending components ### Code Analysis - `analyze_code`: Analyze code and update the knowledge graph ## Example Workflow 1. Initialize a project: ```bash python -m persistent_code init --project-name "TodoApp" ``` 2. Start the server: ```bash python -m persistent_code serve --project-name "TodoApp" ``` 3. Ask Claude to design your project: ``` Can you help me design a Todo app with Python and FastAPI? Let's start with the core data models. ``` 4. Claude will create components and track them in the knowledge graph 5. Continue development in a later session: ``` Let's continue working on the Todo app. What's our implementation status? ``` 6. Claude will retrieve the current status and suggest next steps 7. Implement specific components: ``` Let's implement the task completion endpoint for our Todo app ``` 8. Claude will retrieve relevant context and provide consistent implementation ## Contributing Contributions are welcome! Please feel free to submit a Pull Request. ## License This project is licensed under the MIT License - see the LICENSE file for details.