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Tenets
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About Tenets
Tenets is a free, open-source tool that delivers an MCP server, Python library, and CLI designed to generate intelligent, LLM-ready code context for AI coding assistants. It analyzes project files using NLP and multi-factor relevance scoring methods such as BM25, TF-IDF, import graphs, and optional semantic embeddings to automatically rank and distill the most relevant files for any task. The tool auto-injects project ‘tenets’—guiding principles—into prompts and supports session-based context management, enabling developers to maintain consistent AI-driven guidance across projects. All processing occurs locally, ensuring privacy with no reliance on cloud APIs or external data transmission. Tenets integrates natively with popular AI coding environments like Cursor, Claude Desktop, and Windsurf, allowing developers to feed high-signal context directly into their workflows. This approach accelerates onboarding, reduces context drift, and enforces consistent code guidance by leveraging project-specific insights and structured analysis. The tool also includes features for code quality evaluation, velocity tracking, and dependency visualization, making it useful for both individual developers and teams aiming to improve codebase understanding and maintainability.
Key features
- MCP server, Python library, and CLI for flexible integration
- Multi-factor relevance scoring (BM25, TF-IDF, import graphs, semantic embeddings)
- Auto-injection of project 'tenets' (guiding principles) into prompts
- Session-based context management and summaries
- 100% local processing for privacy preservation
- Native MCP support for Cursor, Claude, and Windsurf
- NLP-powered analysis of project files
- Reduces context drift in AI coding sessions
Use cases
- Enhancing AI coding assistant prompts with high-signal project context
- Accelerating onboarding for new developers by providing relevant project insights
- Enforcing consistent AI-driven code guidance through project tenets
Pros
- Provides intelligent, LLM-ready code context for AI coding assistants
- Uses multi-factor relevance scoring (BM25, TF-IDF, import graphs, semantic embeddings) to rank relevant files
- Supports session-based context management and persistent guiding principles (tenets)
- Processes all data locally, ensuring privacy with no cloud APIs or data leaving the machine
- Offers native MCP integration with Cursor, Claude Desktop, and Windsurf
Cons
- Optional ML features require additional installation (e.g., tenets[ml])
- Advanced features like semantic embeddings may need configuration or API keys
- Primarily command-line and IDE-based, which may not suit users preferring GUI tools
Frequently asked questions about Tenets
What does Tenets do?
Tenets generates intelligent, LLM-ready code context for AI coding assistants by analyzing project files using NLP and multi-factor relevance scoring. It ranks relevant files, distills key information, and injects guiding principles (tenets) into prompts to improve AI-driven code guidance.
Who is Tenets for?
Tenets is designed for developers and teams using AI coding assistants who need to provide high-signal context for tasks like onboarding, debugging, or maintaining consistent coding standards. It is particularly useful for those prioritizing privacy and local processing.
How does Tenets ensure privacy?
Tenets processes all data locally on the user's machine, with no reliance on cloud APIs or external data transmission. This ensures that codebases and sensitive information remain private and secure.
What IDEs does Tenets integrate with?
Tenets supports native MCP integration with Cursor, Claude Desktop, and Windsurf, allowing developers to use its features directly within their preferred coding environments.
Does Tenets require additional setup for advanced features?
Some advanced features, such as semantic embeddings, require additional installation (e.g., tenets[ml]) or configuration. Basic functionality, including multi-factor ranking and local processing, works out of the box.
How do I get started with Tenets?
Getting started involves installing Tenets via pip (e.g., pip install tenets[mcp]), configuring the MCP server in your IDE, and using the CLI or library to analyze and generate context for your projects. Detailed setup instructions are available in the user guide.
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