An MCP server that indexes your codebase into a dependency graph and returns the smallest correct context for any coding task: exact line ranges per symbol, with a rationale for each one.

Runs entirely on your machine. No cloud, no LLM API calls, no embeddings server, no internet connection required. Your source code and task descriptions never leave your laptop. The base install is lightweight: pure Python, no GPU, no heavy ML framework.

pip install claude-context-compiler
cd /your/project
context-compiler init

init indexes your codebase, registers the MCP server with Claude Code, and adds instructions to CLAUDE.md. Open Claude Code and it will call get_context automatically before reading files.

Requires Python 3.11+.

context-compiler init --dependencies ../repo1,../repo2

Each repo is indexed separately. get_context queries all graphs and returns the best-matching symbols across all repos. The dependency list is saved and picked up automatically on next start.

context-compiler index                        # re-index after large changes
context-compiler explain --task "<prompt>"    # preview what context a task returns

pip install "claude-context-compiler[semantic]"

Enables embedding-based fallback for cases where task terms don't appear in symbol names (e.g. "fix login flow" finds authenticate_user). Downloads a 23MB ONNX model once, no PyTorch required.

Every step runs locally with no external calls:

1. Classify the task: BUG_FIX, NEW_FEATURE, or REFACTOR (keyword scoring, no LLM)
2. Find entry nodes via BM25 over symbol names, file paths, and docstrings
3. Traverse the dependency graph with a strategy tuned per task type
4. Score candidates, enforce a token budget, return slices with rationale

The graph is stored in an embedded KuzuDB database in your project folder. No server process, no port, no auth.

Same repo + same task = same output, every time.

{
  "slices": [
    {
      "file_path": "/abs/path/payments/processor.py",
      "line_start": 6,
      "line_end": 24,
      "rationale": "Included PaymentProcessor as primary task location (matched 'payment')"
    },
    {
      "file_path": "/abs/path/payments/retry_handler.py",
      "line_start": 12,
      "line_end": 38,
      "rationale": "Included RetryHandler because it is called by PaymentProcessor (depth 1)"
    }
  ]
}

Each slice points to the specific function or class that's relevant. A 500-line file with one relevant function costs ~40 tokens, not 500.

Apache 2.0