contxt-box 0.1.0

Local-first MCP-compatible persistent knowledge base and media contextualization system.

ConTXT BOX

A local-first external context box for coding agents.

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What Is It?

ConTXT BOX is a strict, local-first knowledge layer that sits beside any project or document folder. It gives coding agents such as Claude Code, Codex, Cursor, and other MCP clients a fast external memory: indexed filenames, folders, neighbors, summaries, cached document/image context, and durable chat preservation.

The design is intentionally narrow. Documents and images are the core path because they cover most real user context. Heavy extraction uses exactly one configured engine: MarkItDown or Docling. No multi-tool fallback chain is used in core extraction.

Features

• Lazy indexing with rel_path, filename, folder, mtime, size, type, neighbors, folder summaries, and cheap file summaries.
• On-demand extraction only through MarkItDown or Docling.
• Permanent Markdown sidecars under .contextbox/history/media/.
• MCP tools for coding agents.
• Watchdog-based watch command for continuous index updates.
• Preview-only smart reorganization.
• Auto preservation into .contextbox/CONTEXT.md plus JSONL history.

Quick Start

uv sync
uv run contxtbox --help
uv run contxtbox init --root "S:\Papers"
uv run contxtbox config-show --root "S:\Papers"
uv run contxtbox index --root "S:\Papers"
uv run contxtbox health --root "S:\Papers"
uv run contxtbox search "computer vision" --root "S:\Papers"

Install the document/image engines:

uv sync --extra media

Extract one file with the strict default engine:

uv run contxtbox extract-media "Computer Vision\paper.pdf" --root "S:\Papers"

Use Docling explicitly:

uv run contxtbox extract-media "Computer Vision\paper.pdf" --root "S:\Papers" --engine docling

Watch a folder:

uv run contxtbox watch --root "S:\Papers"

Run production readiness checks:

uv run contxtbox health --root "S:\Papers" --fail-on-error

Show the effective workspace config:

uv run contxtbox config-show --root "S:\Papers"

Production and MCP setup guides:

• Installation
• Production readiness
• MCP client setup
• Client verification

How It Works

workspace/
`-- .contextbox/
    |-- index.json
    |-- config.toml
    |-- CONTEXT.md
    |-- preservation.jsonl
    `-- history/
        `-- media/
            `-- sanitized__file__path.context.md

Indexing Rules

index, update_index, and watch always record:

• rel_path
• filename
• folder_path
• mtime
• size
• file_type
• neighbors
• parent_folder_summary
• last_indexed
• context_summary

The default summary is cheap and deterministic. It uses filename, folder name, and 5-7 nearby files. It does not open PDFs or images during indexing.

Configuration

init creates .contextbox/config.toml:

extraction_engine = "markitdown"
max_inline_bytes = 512000
large_file_bytes = 50000000
max_neighbors = 10
debounce_seconds = 2.0

ignored_dirs = [
  ".git",
  ".venv",
  "node_modules",
]

priority_folders = [
  "codebases/",
  "research/",
  "specs/",
  "decisions/",
  "assets/images/",
]

Use "docling" when you want Docling as the strict extraction engine.

Extraction Rules

Heavy extraction only happens when:

• extract-media path is called,
• or an MCP client calls get_file(path, depth="full").

The result is cached as Markdown in .contextbox/history/media/, and index.json receives:

• extracted_at
• context_ref
• extraction_method
• extraction_status
• extraction_warnings
• extraction_duration_seconds

Sidecars include the same audit header before extracted content. Status values are conservative: success, partial, metadata-only, or cached.

MCP Tools

• update_index()
• health()
• search(query, limit=10)
• get_file(path, depth="metadata" | "full")
• pull_context(task, limit=5)
• extract_media(path, force=false)
• reorganize(instruction)
• auto_preserve_context(summary, metadata=null)

Start the MCP server:

uv run contxtbox mcp --root "S:\Papers"

Attribution

• Model Context Protocol Python SDK, MIT.
• MarkItDown, MIT.
• Docling, MIT.
• watchdog, Apache-2.0.
• sentence-transformers, Apache-2.0 library with model-specific licenses.
• ChromaDB, Apache-2.0.
• gstack, MIT, as workflow inspiration.
• Ponytail, MIT, as minimal-agent behavior inspiration.

Roadmap

• Stronger semantic search over sidecars.
• Reorganization scoring based on folder summaries and neighbor cues.
• MCP client recipes for Claude Code, Codex, Cursor, and others.
• Safe apply/undo flow for reorganization.
• Configurable ignore rules and extraction engine policy.

License

MIT. See LICENSE.

Release

PyPI publishing is configured for Trusted Publishing through GitHub Actions. See Production readiness.