context-compiler-mcp 1.1.0

Local context compiler for AI coding assistants — smallest correct context bundle with rationale

context-compiler

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.

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Getting started

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

By default init registers with Claude Code. Pass --client to target a different assistant:

context-compiler init --client claude    # Claude Code (default)
context-compiler init --client gemini    # Gemini CLI
context-compiler init --client codex     # OpenAI Codex CLI

init indexes your codebase and registers the MCP server with your chosen assistant. For Claude Code it also adds instructions to CLAUDE.md so it calls get_context automatically before reading files.

Requires Python 3.11+.

Multi-repo projects

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

Other commands

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

Optional: semantic search

pip install "context-compiler-mcp[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.

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How it works

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.

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Output

{
  "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.

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Supported languages

Language	Parsing
Python	tree-sitter-python
TypeScript / TSX	tree-sitter-typescript
JavaScript / JSX	tree-sitter-javascript

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License

Apache 2.0