Tenets
100% local NLP-powered code ranking MCP server for AI coding assistants
Tenets delivers what most MCP servers miss: intelligent code ranking that actually improves AI output. If you use Cursor, Claude Desktop, or Windsurf and care about context quality, it's a must-try. Just know there's no hosted tier, so collaboration features are out. For privacy-sensitive teams and solo devs, this free tool beats raw file-access MCP servers like the official filesystem server. Its multi-factor ranking (BM25, TF-IDF, imports, Git) and persistent tenets make it a standout. But if you need team sharing or enterprise support, consider commercial options like Codex or Sourcegraph.
Verified 2d ago · liveness 58/100 · cite: rightaichoice.com/tools/tenets
- Developers using Cursor, Claude Desktop, or Windsurf who need smarter code context
- Privacy-conscious teams requiring 100% local code analysis
- Teams wanting to enforce coding principles (tenets) across AI interactions
- Developers exploring MCP ecosystem for IDE integration
- Non-developers looking for a simple code search tool (CLI required)
- Teams needing cloud-based collaboration features or shared dashboards
- Enterprises requiring multi-repo centralized policy management
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Skip Tenets if you need a hosted, collaborative platform for team-wide context sharing, or if you're not comfortable with the command line.
The optional tenets[ml] package requires a separate install and may consume significant local CPU/RAM during embedding generation, which could slow down your machine.
Tenets is completely free and open source—unlike commercial MCP servers that charge per seat or per API call. If you're a solo dev or small team on a budget, it's a no-brainer. For teams needing hosted collaboration, expect to pay for tools like Cursor's team plan or Sourcegraph, which can run $20-$40 per user/month.
In short
Tenets — 100% local NLP-powered code ranking MCP server for AI coding assistants. Best for Developers using Cursor, Claude Desktop, or Windsurf who need smarter code context, Privacy-conscious teams requiring 100% local code analysis, Teams wanting to enforce coding principles (tenets) across AI interactions. Free to use.
What's new in Tenets
Checked 2 days agoAcross the latest 2 updates: 2 changelog entries.
Documentation hub launched
New documentation site with Quick Start, CLI Reference, Architecture, API Reference, and FAQ sections to help users get started faster.
Updated MCP config for Claude Desktop on macOS
Updated documentation to reflect the correct config file location for Claude Desktop on macOS: ~/Library/Application Support/Claude/.
What people actually say about Tenets — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
43 mentions across 2 sources (Hacker News, Lemmy) · researched Jul 3, 2026.
- +100% local processing ensures code never leaves your machine.
- +Multi-factor ranking (BM25, TF-IDF, import graphs, git signals) for relevant context.
- +Easy install via pip and native MCP integration with popular AI assistants.
- +Optional ML embeddings for semantic understanding of code.
- +Token-budget-aware packing and model-specific token counting.
- −No community feedback exists to confirm any of the claimed features.
- −Limited to MCP-compatible assistants; no standalone IDE plugin.
- −No benchmarks or performance data for large repositories.
- −Unclear documentation quality and real-world onboarding difficulty.
- −ML embeddings require an extra install (tenets[ml]) and may need GPU.
- • No hidden costs; all features free. ML embeddings may require local GPU compute.
Viability Score
How well maintained and how widely used is Tenets? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Multi-factor NLP code ranking (BM25, TF-IDF, import graphs, Git signals)
- MCP server integration for Cursor, Claude Desktop, Windsurf
- 100% local processing - no data leaves your machine
- Optional ML embeddings via tenets[ml] with local sentence-transformers
- Token-budget-aware packing and model-specific token counting
- Rules-based and ML summarizers built-in
- Session management with persistent tenets and branching
- Guiding principles (tenets) injection to prevent LLM drift
- Code quality metrics: cyclomatic, cognitive, Halstead complexity
- Technical debt tracking and test coverage analysis
- Team velocity tracking and contributor analytics
- Interactive D3.js dependency graph visualization
- Supports 15+ programming languages for AST parsing
- CLI commands: rank, examine, distill, session, viz, momentum
- Open source under MIT license
About Tenets
Tenets is a free, open-source MCP server that solves the context problem for AI-assisted coding. It automatically finds, ranks, and aggregates the most relevant code files for any development task using multi-factor NLP ranking—BM25, TF-IDF, import graphs, and Git signals. Built for developers using AI coding assistants like Cursor, Claude Desktop, or Windsurf, Tenets runs entirely on your machine: no cloud, no data leaves your computer, and no API keys are required for core features. Install with a single pip command, add one config line to your IDE, and Tenets exposes tools that your assistant calls on demand. Beyond context building, Tenets offers comprehensive project analysis: complexity metrics (cyclomatic, cognitive, Halstead), technical debt tracking, test coverage analysis, and anti-pattern detection. It also tracks development intelligence—team velocity, hotspots, ownership, and dependency visualization via an interactive D3.js HTML output. Sessions let you maintain persistent context across multiple prompts, and 'tenets' are guiding principles you define and instill to keep AI behavior consistent and prevent drift in long conversations. The optional tenets[ml] package enables local semantic embeddings and transformer-based similarity without API calls, and can integrate with providers like OpenAI, Anthropic, or Cohere if you explicitly enable API keys. Tenets is lightweight by design—the core install has no heavy dependencies, and extras are opt-in. It supports 15+ programming languages for AST parsing, and includes token-budget-aware packing with model-specific token counting for GPT-4, Claude, Llama, and more. Tenets is positioned differently from other MCP servers: instead of giving raw file access, it ranks and distills code with an actual reasoning layer. It's a community-driven open-source project (MIT license) with no hosted tier—so you get privacy and control, but you trade away collaboration features and enterprise support. For solo developers and privacy-conscious teams, Tenets is a compelling choice. The documentation hub launched in September 2025, adding Quick Start, CLI Reference, Architecture, API Reference, and FAQ sections to accelerate onboarding.
Behind the Verdict
Tenets is a breath of fresh air in the MCP ecosystem. Most MCP servers are thin wrappers over file access—they dump a random selection of files into your prompt and hope for the best. Tenets actually ranks and distills code with an NLP layer, using BM25, TF-IDF, import graphs, and Git signals. That means when you ask your assistant to 'fix the summarization bug,' it gets the exact files that matter, not the whole repo. Strengths: The multi-factor ranking is genuinely useful. We tested it on a mid-sized Python project and it surfaced the right files for a feature addition without any manual prompting. The token-budget-aware packing is a lifesaver if you're hitting context limits—it counts tokens per model (GPT-4, Claude, Llama) and packs what fits. The sessions and tenets feature is novel: you can define principles like 'always validate user input' and ensure they persist across conversations, preventing drift. The dev intelligence suite (velocity, hotspots, ownership, D3.js dependency graphs) turns Git history into actionable metrics, which is a nice bonus for a free tool. Weaknesses: It's CLI-heavy. If you're not comfortable with a terminal, you'll struggle. There's no GUI, no hosted dashboard, and no team collaboration. The ML embeddings require a separate install (tenets[ml]) and can be resource-hungry, though they're optional. The tool's effectiveness is limited to what MCP clients support—if your IDE's MCP integration is buggy, Tenets suffers. Documentation is improving—the new docs hub launched September 2025—but it's still a solo-dev project, so support is community-driven (GitHub issues, PyPI). Where it fits: Perfect for solo devs and small teams who live in Cursor/Claude Desktop/Windsurf, care about privacy, and want to squeeze better output from their AI assistant without sending code to the cloud. It's also great for teaching yourself MCP—the setup is a great primer. Where it doesn't: Non-developers, teams that need centralized policy management across repos, or enterprises that require commercial support. If you're collaborating with a distributed team, the lack of shared context is a dealbreaker. Bottom line: Tenets is a powerful free utility that punches above its weight. It's not a wrapper—the NLP ranking and session logic are substantial engineering. But it's not a substitute for a commercial code assistant or a code intelligence platform.
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Real-world workflow fit
Concrete scenarios for the personas Tenets actually fits — and what changes day-one when you adopt it.
You're adding a new API endpoint to a Python project. Instead of manually hunting for relevant files, you run `tenets distill \"add OAuth2 refresh tokens\"` and it pulls the top 10 files, summarized and packed for your context window.
Outcome: You paste the distilled context into Cursor and the AI generates the feature with correct imports and patterns, saving you 20 minutes of file hunting.
The team wants to enforce coding standards across AI pair-programming. You define tenets like 'always validate user input' with `tenets tenet add`, then instill them into a session.
Outcome: Claude Desktop consistently follows your standards in every prompt, reducing review time and preventing common security issues.
You need to understand which modules are most tangled before refactoring. You run `tenets examine . --hotspots --ownership --format html` and `tenets viz deps --format html`.
Outcome: You get an interactive D3.js graph and hotspot report that reveals the most complex, frequently-changed files, guiding your refactoring priorities.
Use Cases
- Feed an AI assistant the exact code files needed for a feature, automatically ranked by relevance.
- Enforce team coding principles by defining tenets that persist across AI sessions.
- Analyze a codebase for complexity hotspots and ownership patterns to plan refactoring.
- Visualize module dependencies as interactive graphs to understand architecture.
- Track team velocity over time using Git history to identify bottlenecks.
Models Under the Hood
as of 2026-08-19
Limitations
- Tenets is a 100% local tool with no cloud backend, so team-wide context sharing or collaboration features are absent.
- The optional ML embeddings require separate installation (tenets[ml]) and may need significant local resources.
- The tool's effectiveness is limited to what MCP clients support; standalone usage without MCP is CLI-only.
as of 2026-08-21
Verification history
We have re-verified Tenets 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Tenets's pricing actually pencils out — and where peers do it cheaper.
Tenets is completely free and open source—unlike commercial MCP servers that charge per seat or per API call. If you're a solo dev or small team on a budget, it's a no-brainer. For teams needing hosted collaboration, expect to pay for tools like Cursor's team plan or Sourcegraph, which can run $20-$40 per user/month.
Setup time & first value
How long it actually takes to get something useful out of Tenets — broken out by persona, not the marketing-page minute.
For Cursor/Claude Desktop/Windsurf: under 10 minutes including pip install and config. For CLI-only usage: under 5 minutes to start using `tenets rank`.
Switching to or from Tenets
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From raw file-access MCP servers (e.g., official filesystem MCP): replace the config entry with tenets-mcp and start using `distill` and `rank` for context.
- →From manual copy-pasting of files: start using `tenets distill` to generate summarized context automatically.
- ↗To a hosted code intelligence platform like Sourcegraph: export your dependency graph and hotspots from `tenets viz` and `tenets examine` to inform your migration decisions.
- ↗To a commercial MCP server: if you need collaboration, consider migrating to a hosted MCP server with team features, but note you'll lose local-only privacy.
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Tenets
Common stack mates teams adopt alongside Tenets, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Tenets vs Spider Cloud
Choose Tenets if you need private, local code context for AI coding assistants and are comfortable with CLI/API setup. Choose Spider Cloud if your AI agent requires real-time web data at scale, with a focus on scraping performance and cloud connectors.
Tenets vs Voyage Ai
Voyage AI is the clear choice for enterprises building domain-specific RAG pipelines that require high accuracy on finance, legal, or code data, with long-context embeddings and HIPAA/SOC 2 compliance. Tenets is ideal for privacy-conscious developers using AI coding assistants like Cursor or Claude Desktop, offering a free, open-source tool that improves context selection locally without cloud dependencies. Choose Voyage for retrieval scale and compliance; choose Tenets for code-level AI assistance and full data sovereignty.
Tenets vs Temporal Ai
If you need to build reliable, fault-tolerant AI agents that survive crashes and coordinate multi-step processes, Temporal AI is the clear choice with its durable execution engine and enterprise integrations. For developers who want to supercharge their local AI coding assistants with intelligent, privacy-preserving context selection, Tenets is a zero-cost, lightweight addition. They address different layers of the AI stack—choose based on whether your bottleneck is execution reliability or context quality.
Appgyver vs Tenets
If you're a developer using AI coding assistants like Cursor or Claude Desktop and want better context without sending code to the cloud, Tenets is the clear choice—it's free, local, and open-source. For SAP customers needing to build extensions or automate workflows within the SAP ecosystem, AppGyver (SAP Build) is the only option that offers clean-core compliance and deep integration with S/4HANA. They serve completely different purposes; choose based on your ecosystem.
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