tenets

MCP server for context that feeds your prompts.

Intelligent code context aggregation + automatic guiding principles injection—100% local.

Coverage note: Measures core modules (distiller, ranking, MCP, CLI, models). Optional features (viz, language analyzers) are excluded.

tenets is an MCP server for AI coding assistants. It solves two critical problems:

1. Intelligent Code Context — Finds, ranks, and aggregates the most relevant code using NLP (BM25, TF-IDF, import centrality, git signals). No more manual file hunting.

2. Automatic Guiding Principles — Injects your tenets (coding standards, architecture rules, security requirements) into every prompt automatically. Prevents context drift in long conversations.

Integrates natively with Cursor, Claude Desktop, Windsurf, VS Code via Model Context Protocol. Also ships a CLI and Python library. 100% local processing — no API costs, no data leaving your machine.

What is tenets?

• Finds all relevant files automatically using NLP analysis
• Ranks them by importance using BM25, TF-IDF, ML embeddings, and git signals
• Aggregates them within your token budget with intelligent summarizing
• Injects guiding principles (tenets) automatically into every prompt for consistency
• Integrates natively with AI assistants via Model Context Protocol (MCP)
• Pins critical files per session for guaranteed inclusion
• Transforms content on demand (strip comments, condense whitespace, or force full raw context)

MCP-first Quickstart (recommended)

• Install + start MCP server

  pip install tenets[mcp]
  tenets-mcp
  

• Claude Code (CLI / VS Code extension)

  claude mcp add tenets -s user -- tenets-mcp
  

  Or manually add to ~/.claude.json:

  { "mcpServers": { "tenets": { "type": "stdio", "command": "tenets-mcp", "args": [] } } }
  

• Claude Desktop (macOS app - ~/Library/Application Support/Claude/claude_desktop_config.json)

  { "mcpServers": { "tenets": { "command": "tenets-mcp" } } }
  

• Cursor (~/.cursor/mcp.json)

  { "mcpServers": { "tenets": { "command": "tenets-mcp" } } }
  

• Windsurf (~/.windsurf/mcp.json)

  { "tenets": { "command": "tenets-mcp" } }
  

• VS Code Extension (alternative for VS Code users)
  • Install from VS Code Marketplace ⭐
  • Or search "Tenets MCP Server" in VS Code Extensions
  • Extension auto-starts the server and provides status indicator + commands
• Docs (full tool list & transports): https://tenets.dev/MCP/

Installation (CLI/Python)

# Using pipx (recommended for CLI tools)
pipx install tenets[mcp]     # MCP server + CLI (recommended)
pipx install tenets          # CLI only (no MCP server)

# Or using pip
pip install tenets[mcp]      # Adds MCP server dependencies (REQUIRED for MCP)
pip install tenets           # CLI + Python, BM25/TF-IDF ranking (no MCP)
pip install tenets[light]    # RAKE/YAKE keyword extraction
pip install tenets[viz]      # Visualization features
pip install tenets[ml]       # ML embeddings / reranker (2GB+)
pip install tenets[all]      # Everything

Important: The [mcp] extra is required for MCP server functionality. Without it:

• The tenets-mcp executable exists but will fail when you try to run it
• Missing dependencies: mcp, sse-starlette, uvicorn (15 additional packages)
• You'll get a clear error: ImportError: MCP dependencies not installed

MCP Tool Surface (AI assistants)

• Start the MCP server

  pip install tenets[mcp]
  tenets-mcp
  

• Cursor (~/.cursor/mcp.json)

  {
    "mcpServers": {
      "tenets": { "command": "tenets-mcp" }
    }
  }
  

• Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json)

  {
    "mcpServers": {
      "tenets": { "command": "tenets-mcp" }
    }
  }
  

• Tools exposed: distill, rank, examine, session_*, tenet_* (same surface as CLI).
• Docs: see docs/MCP.md for full endpoint/tool list, SSE/HTTP details, and IDE notes.

MCP Server (AI assistant integration)

Once you start tenets-mcp and drop one of the configs above into your IDE, ask your AI:

• “Use tenets to find the auth code” (calls distill)
• “Pin src/auth to session auth-feature” (calls session_pin_folder)
• “Rank files for the payment bug” (calls rank_files)

See MCP docs for transports (stdio/SSE/HTTP), tool schemas, and full examples.

Quick Start

Three Ranking Modes

Tenets offers three modes that balance speed vs. accuracy for both distill and rank commands:

Mode	Speed	Accuracy	Use Case	What It Does
fast	Fastest	Good	Quick exploration	Keyword & path matching, basic relevance
balanced	1.5x slower	Better	Most use cases (default)	BM25 scoring, keyword extraction, structure analysis
thorough	4x slower	Best	Complex refactoring	ML semantic similarity, pattern detection, dependency graphs

Core Commands

distill - Build Context with Content

# Basic usage - finds and aggregates relevant files
tenets distill "implement OAuth2"  # Searches current directory by default

# Search specific directory
tenets distill "implement OAuth2" ./src

# Copy to clipboard (great for AI chats)
tenets distill "fix payment bug" --copy

# Generate interactive HTML report
tenets distill "analyze auth flow" --format html -o report.html

# Speed/accuracy trade-offs
tenets distill "debug issue" --mode fast       # <5s, keyword matching
tenets distill "refactor API" --mode thorough  # Semantic analysis

# ML-enhanced ranking (requires pip install tenets[ml])
tenets distill "fix auth bug" --ml              # Semantic embeddings
tenets distill "optimize queries" --ml --reranker  # Neural reranking (best accuracy)

# Transform content to save tokens
tenets distill "review code" --remove-comments --condense

# Adjust timeout (default 120s; set 0 to disable)
tenets distill "implement OAuth2" --timeout 180

rank - Preview Files Without Content

# See what files would be included (much faster than distill!)
tenets rank "implement payments" --top 20  # Searches current directory by default

# Understand WHY files are ranked
tenets rank "fix auth" --factors

# Tree view for structure understanding
tenets rank "add caching" --tree --scores

# ML-enhanced ranking for better accuracy
tenets rank "fix authentication" --ml           # Uses semantic embeddings
tenets rank "database optimization" --ml --reranker  # Cross-encoder reranking

# Export for automation
tenets rank "database migration" --format json | jq '.files[].path'

# Search specific directory
tenets rank "payment refactoring" ./src --top 10

Sessions & Guiding Principles (Tenets)

The killer feature: define guiding principles once, and they're automatically injected into every prompt.

# Create a working session
tenets session create payment-feature

# Add guiding principles (tenets) — these auto-inject into all prompts
tenets tenet add "Always validate user inputs before database operations" --priority critical
tenets tenet add "Use Decimal for monetary calculations, never float" --priority high
tenets tenet add "Log all payment state transitions" --priority medium

# Pin critical files (guaranteed inclusion in context)
tenets session pin-file payment-feature src/core/payment.py

# Instill tenets to the session
tenets instill --session payment-feature

# Now every distill automatically includes your tenets + pinned files
tenets distill "add refund flow" --session payment-feature
# Output includes: relevant code + your 3 guiding principles

Why this matters: In long AI conversations, context drifts. The AI forgets your coding standards. Tenets solve this by re-injecting your rules every time.

Other Commands

# Visualize architecture
tenets viz deps --output architecture.svg   # Dependency graph
tenets viz deps --format html -o deps.html  # Interactive HTML

# Track development patterns
tenets chronicle --since "last week"        # Git activity
tenets momentum --team                      # Sprint velocity

# Analyze codebase
tenets examine . --complexity --threshold 10  # Find complex code

Configuration

Create .tenets.yml in your project:

ranking:
  algorithm: balanced # fast | balanced | thorough
  threshold: 0.1
  use_git: true # Use git signals for relevance

context:
  max_tokens: 100000

output:
  format: markdown
  copy_on_distill: true # Auto-copy to clipboard

ignore:
  - vendor/
  - '*.generated.*'

How It Works

Code analysis intelligence

tenets employs a multi-layered approach optimized specifically for code understanding (but its core functionality could be applied to any field of document matching). It tokenizes camelCase and snake_case identifiers intelligently. Test files are excluded by default unless specifically mentioned in some way. Language-specific AST parsing for 15+ languages is included.

Multi-ranking NLP

Deterministic algorithms in balanced work reliably and quickly meant to be used by default. BM25 scoring prevents biasing of files which may use redundant patterns (test files with which might have "response" referenced over and over won't necessarily dominate searches for "response").

The default ranking factors consist of: BM25 scoring (25% - statistical relevance preventing repetition bias), keyword matching (20% - direct substring matching), path relevance (15%), TF-IDF similarity (10%), import centrality (10%), git signals (10% - recency 5%, frequency 5%), complexity relevance (5%), and type relevance (5%).

Smart Summarization

When files exceed token budgets, tenets intelligently preserves:

• Function/class signatures
• Import statements
• Complex logic blocks
• Documentation and comments
• Recent changes

ML / deep learning embeddings

Semantic understand can be had with ML features: pip install tenets[ml]. Enable with --ml --reranker flags or set use_ml: true and use_reranker: true in config.

In thorough mode, sentence-transformer embeddings are enabled, and understand that authenticate() and login() are conceptually related for example, and that payment even has some crossover in relevancy (since these are typically associated together).

Optional cross-encoder neural re-ranking in this mode jointly evaluates query-document pairs with self-attention for superior accuracy.

A cross-encoder, for example, will correctly rank "DEPRECATED: We no longer implement oauth2" lower than implement_authorization_flow() for query "implement oauth2", understanding the negative context despite keyword matches.

Since cross-encoders process document-query pairs together (O(n²) complexity), they're much slower than bi-encoders and only used for re-ranking top K results.

Documentation

• Full Documentation - Complete guide and API reference
• CLI Reference - All commands and options
• Configuration Guide - Detailed configuration options
• Architecture Overview - How tenets works internally

Output Formats

# Markdown (default, optimized for AI)
tenets distill "implement OAuth2" --format markdown

# Interactive HTML with search, charts, copy buttons
tenets distill "review API" --format html -o report.html

# JSON for programmatic use
tenets distill "analyze" --format json | jq '.files[0]'

# XML optimized for Claude
tenets distill "debug issue" --format xml

Python API

from tenets import Tenets

# Initialize
tenets = Tenets()

# Basic usage
result = tenets.distill("implement user authentication")
print(f"Generated {result.token_count} tokens")

# Rank files without content
from tenets.core.ranking import RelevanceRanker
ranker = RelevanceRanker(algorithm="balanced")
ranked_files = ranker.rank(files, prompt_context, threshold=0.1)

for file in ranked_files[:10]:
    print(f"{file.path}: {file.relevance_score:.3f}")

Supported Languages

Specialized analyzers for Python, JavaScript/TypeScript, Go, Java, C/C++, Ruby, PHP, Rust, and more. Configuration and documentation files are analyzed with smart heuristics for YAML, TOML, JSON, Markdown, etc.

Contributing

See CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE for details.

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