docctx
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@docctxsearch docs for useEffect cleanup"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
docctx
Local-first deterministic context retrieval engine for coding agents.
What is docctx?
docctx solves a specific problem: when a coding agent needs documentation, it delivers the most precise chunks from the most trusted sources — with explainable ranking — or returns nothing.
Key principle: Wrong context is more dangerous than no context. An agent with no docs knows it doesn't know. An agent with wrong docs confidently generates wrong code.
Related MCP server: agentmako
Installation
# Install with uv (recommended)
uv pip install -e .
# Or with pip
pip install -e .Quick Start
# Add documentation
docctx add https://react.dev/reference/react/useEffect
# List packs
docctx list
# Query documentation
docctx query "useEffect cleanup"
# Start MCP server
docctx serveCLI Commands
Command | Description |
| Ingest a URL as a context pack |
| Re-crawl an existing pack |
| Hard delete a pack |
| List all packs |
| Search documentation |
| Inspect extraction or pack structure |
| Show retrieval reasoning |
| Health check |
| Start MCP server |
MCP Configuration
Add to your MCP client config (e.g. Claude Desktop claude_desktop_config.json):
{
"mcpServers": {
"docctx": {
"command": "docctx",
"args": ["serve"]
}
}
}Or with uv run:
{
"mcpServers": {
"docctx": {
"command": "uv",
"args": ["run", "--project", "/path/to/docctx", "docctx", "serve"]
}
}
}MCP Tools
search_docs— Search chunks. Supportsresponse_mode: compact|standard,token_budget,min_confidence: high|low|anyget_chunk— Get full chunk content by ID. Lazy expansion aftersearch_docs.list_packs— Discover available documentation packs.
Configuration
Config file: ~/.docctx/config.toml (auto-created on first run)
[retrieval]
floor_score = 3.0 # BM25 minimum to include
confidence_cutoff = 6.0 # BM25 threshold for "high" confidence
default_limit = 5
max_limit = 10
[chunking]
target_tokens = 400
max_tokens = 800
min_tokens = 80
[ingestion]
rate_limit_rps = 1.0
max_pages = 50
max_depth = 2
respect_robots = trueScope Rules
Scope | Crawls |
| Entry URL only |
| Entry URL + sibling pages (same parent path) — default for deep URLs |
| Entry URL + all descendants |
| Entire domain (requires |
Development
# Install dev dependencies
uv pip install -e ".[dev]"
# Run tests
pytest
# Run specific test file
pytest tests/unit/test_chunker.py -vArchitecture
INGESTION (CLI, network):
URL → Discover → Fetch → Extract → Chunk → Index → DB
SERVING (MCP, offline):
Query → FTS5 → Boost → Filter → Threshold → ChunksStorage: ~/.docctx/store.db (SQLite WAL)
Cache: ~/.docctx/cache/ (SHA256-keyed HTML)
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