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jddunnMCP SERVER · OFFICIAL PROJECT

Tenets

Gather the code that actually matters for a task, and re-inject your coding rules into every prompt so long conversations stop drifting.

by jddunn ↗Codebase Context Packing & Compression✓ Official

LABSCO SUMMARY

The tenets are the point — standards get re-injected, not remembered.

Context retrieval is the visible half, but the thing that changes long sessions is re-injection: rules like "use Decimal for money, never float" go back into every generated context, so the model does not quietly forget them at turn forty. Two practical notes — the [mcp] extra is mandatory and its absence fails at runtime rather than install, and the rank tool is the cheap way to see what would be included before paying for a full distill.

What it is

A context server for coding assistants doing two jobs. It ranks and aggregates the relevant files for a request using BM25, TF-IDF, import centrality and git signals — and it injects your guiding principles, the tenets, into generated context so standards survive a long session. All processing is local; no code leaves the machine.

What you get

  • tenets_distill finds and returns the most relevant code with metadata; tenets_rank_files lists the same ranking without fetching content, at roughly 500ms against about 3s
  • tenets_session manages persistent sessions with create, list, pin_file and pin_folder — pinned files are always included
  • tenets_tenet adds and lists guiding principles at critical, high, medium or low priority, and instills them into a session
  • tenets_system_instruction sets a one-time instruction injected into all generated context
  • tenets_examine reports structure, complexity and hotspots; tenets_chronicle reads git history; tenets_momentum tracks velocity
  • tenets_search_tools and tenets_get_tool_schema let the model discover the rest on demand instead of loading every schema
  • Three ranking modes: fast for keyword and path matching, balanced with BM25 by default, thorough with ML semantic similarity and dependency graphs

Requirements

The Python package tenets, version 0.13.3, installed with the mcp extra — pip install tenets[mcp] or pipx install tenets[mcp]. Without that extra the tenets-mcp executable exists but fails at launch with an ImportError. Register it with claude mcp add tenets -s user -- tenets-mcp, or add {"command": "tenets-mcp"} to your client's MCP config. Python 3.9+. Semantic embeddings and the reranker need tenets[ml], which is a 2GB+ install.

Setup effort

One command — pipx install tenets[mcp]

See installation options