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MCP server · AI

Quarry

Local code intelligence engine for AI coding agents. Answers "what breaks if I change this" and finds the right file for 99.4% fewer tokens than grep and read. Fully local, no GPU, no upload.

64Good

Scored a month ago · breakdown

About Quarry

Quarry is an MCP server published by punt-labs in the AI category: local code intelligence engine for AI coding agents. Answers "what breaks if I change this" and finds the right file for 99.4% fewer tokens than grep and read. Fully local, no GPU, no upload.

Recent commit activity is the strongest signal that reported issues will get attention.

Install

From source
git clone https://github.com/punt-labs/quarry

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

Tools (7)

deleteRemove a document or collection

Parameters not published.

findSemantic search with filters

Parameters not published.

listDocuments, collections, databases, registrations

Parameters not published.

showDocument metadata or page text

Parameters not published.

statusDatabase statistics

Parameters not published.

sync_all_registrationsRe-index all registered directories

Parameters not published.

useSwitch the active database

Parameters not published.

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Security checks

  • ✓README presentThe repository ships documentation.
  • ✓License declaredUsable under a stated license.
  • !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.

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Releases

v3.4.0v3.4.0 · 21 Sep 2026What's Changed chore: update README install SHA v3.3.0 by @jmf-pobox in https://github.com/punt-labs/quarry/pull/526 chore: post-release v3.3.0 by @jmf-pobox in https://github.com/punt-labs/quarry/pull/527 Remove the misleading doctor…
v3.3.0v3.3.0 · 20 Sep 2026What's Changed docs(changelog): backfill v3.2.1 entries for quarry-ridg + quarry-jzqw by @jmf-pobox in https://github.com/punt-labs/quarry/pull/509 chore: restore dev plugin state after v3.2.1 release by @jmf-pobox in…
v3.2.1v3.2.1 · 3 Sep 2026What's Changed chore: post-release v3.2.0 by @jmf-pobox in https://github.com/punt-labs/quarry/pull/499 test(hooks): failing regression tests for jzqw, ridg, 871u (quarry-yndv) by @jmf-pobox in https://github.com/punt-labs/quarry/pull/500…
v3.2.0v3.2.0 · 1 Sep 2026What's Changed chore: post-release v3.1.0 by @jmf-pobox in https://github.com/punt-labs/quarry/pull/492 chore: tidy paths and fixtures in docs and tests by @jmf-pobox in https://github.com/punt-labs/quarry/pull/495 fix(plugin):…
v3.1.0v3.1.0 · 1 Sep 2026What's Changed chore: post-release v3.0.3 by @claude-puntlabs in https://github.com/punt-labs/quarry/pull/464 fix(ci): wire two verification gates into the CI graph where they can actually catch what they're for by @claude-puntlabs in…

README

punt-quarry

Local semantic search for AI agents and humans.

License CI PyPI Python Working Backwards

Quarry indexes documents in 20+ formats, embeds them with a local ONNX model (snowflake-arctic-embed-m-v1.5, 768-dim), stores vectors in LanceDB, and serves semantic search to Claude Code, Claude Desktop, and the CLI. Everything runs locally — no API keys, no cloud accounts. The embedding model (~120 MB int8) downloads once on first use. CUDA GPUs are auto-detected for faster inference.

Platforms: macOS, Linux

Quick Start

curl -fsSL https://raw.githubusercontent.com/punt-labs/quarry/1961675/install.sh | sh

Restart Claude Code, then:

> /ingest report.pdf                    # index a document (runs in background)
> /quarry status                        # after a moment, confirm it's there
> /find "what does the report say about margins"   # search by meaning

Once installed, a plugin hook auto-indexes your current project directory on every session start — you don't need to /ingest your codebase manually.

uv tool install punt-quarry
quarry install
quarry doctor
curl -fsSL https://raw.githubusercontent.com/punt-labs/quarry/1961675/install.sh -o install.sh
shasum -a 256 install.sh
cat install.sh
sh install.sh

Remote Server

Run quarry on a GPU server and connect from any Mac or Linux client over TLS.

Server (GPU host, serves remote clients):

export QUARRY_API_KEY=$(openssl rand -hex 32)
curl -fsSL https://raw.githubusercontent.com/punt-labs/quarry/1961675/install.sh | sh -s -- --network

Generates TLS certificates, binds daemon to 0.0.0.0, registers a systemd service, and prints a CA fingerprint. NVIDIA GPUs are auto-detected for CUDA inference.

Client (connects to remote server):

curl -fsSL https://raw.githubusercontent.com/punt-labs/quarry/1961675/install.sh | sh
quarry login <server-hostname> --api-key <token>

No special flag needed --- the default install runs a local daemon on localhost. quarry login redirects queries to the remote server over wss:// with TOFU certificate pinning.

Claude Desktop

Download punt-quarry.mcpb and double-click to install. Alternatively, quarry install configures Claude Desktop automatically.

Note: Uploaded files in Claude Desktop live in a sandbox that quarry cannot access. Use remember for uploaded content, or provide local file paths to ingest.

Features

  • 20+ formats --- PDFs (with OCR for scanned pages), source code (AST-aware splitting), spreadsheets, presentations, HTML, Markdown, LaTeX, DOCX, images
  • Semantic search --- retrieval is by meaning, not keyword. A query about "margins" finds passages about profitability even if they never use that word
  • Daemon architecture --- one quarry serve process loads the embedding model once and serves all Claude Code sessions via mcp-proxy over WebSocket
  • Passive knowledge capture --- SessionStart hook auto-indexes the working directory, PostToolUse hook auto-ingests fetched URLs, PreCompact hook captures transcripts before context compaction
  • Named databases --- isolated LanceDB directories with independent sync registries. Switch with use for work/personal separation
  • Research agent --- researcher subagent combines quarry local search with web research, auto-ingests valuable findings

What It Looks Like

Ingest a document

> /ingest report.pdf

▶ Ingesting report.pdf (background)

Check what's indexed

> /quarry

▶ Database: default
  Documents: 47
  Chunks: 1,203
  Size: 12.4 MB
  Model: snowflake-arctic-embed-m-v1.5 (768-dim)

Search by meaning

> /find "what were the Q3 revenue figures"

▶ [report.pdf p.12 | text/.pdf] (similarity: 0.4521)
  Third quarter revenue reached $142M, up 18% year-over-year,
  driven primarily by expansion in the enterprise segment.
  Gross margins improved to 71% from 68% in Q2.

Commands

Slash Commands (Claude Code)

Command What it does
/ingest <source> Ingest a URL, directory, or file
/remember <name> Ingest inline text under a document name
/find <query> Semantic search. Questions get synthesized answers; keywords get raw results
/explain <topic> Search and synthesize an explanation
/source <claim> Find which document a claim comes from
/quarry [sub] Manage: status, sync, collections, databases, registrations

MCP Tools

Tool Purpose Execution
ingest Index a file or URL Background
remember Index inline text Background
register_directory Register directory for sync Background
sync_all_registrations Re-index all registered directories Background
find Semantic search with filters Sync
show Document metadata or page text Sync
list Documents, collections, databases, registrations Sync
status Database statistics Sync
delete Remove document or collection Background
deregister_directory Remove registration Background
use Switch active database Sync

CLI

quarry ingest report.pdf                       # index a file
quarry ingest https://example.com              # index a webpage
echo "notes" | quarry remember --name notes.md # index inline text
quarry find "revenue trends"                   # hybrid search (vector + FTS)
quarry list documents                          # list indexed documents
quarry register ~/Documents/notes              # watch a directory
quarry sync                                    # re-index registered dirs
quarry use work                                # switch database
quarry status                                  # database dashboard
quarry doctor                                  # health check
quarry serve                                   # start daemon on :8420
quarry install                                 # set up daemon, TLS certs, mcp-proxy

# Remote connections
quarry login okinos.local --api-key <token>    # TOFU login to remote server
quarry logout                                  # disconnect, revert to local daemon
quarry remote list --ping                      # show remote config and health

# Agent memory tagging
quarry ingest notes.md --agent-handle claude --memory-type fact
quarry find "deployment steps" --agent-handle claude
echo "key insight" | quarry remember --name insight.md --agent-handle claude \
  --memory-type observation --summary "Key insight from review"

Setup

Quarry works with zero configuration. These environment variables are available for customization:

Variable Default Description
QUARRY_PROVIDER (auto) ONNX execution provider: cpu, cuda, or unset (auto-detect)
QUARRY_API_KEY (none) Bearer token for quarry serve
QUARRY_ROOT ~/.punt-labs/quarry/data Base directory for all databases
CHUNK_MAX_CHARS 1800 Max characters per chunk (~450 tokens)
CHUNK_OVERLAP_CHARS 200 Overlap between consecutive chunks

For the full configuration reference, see Architecture section 7.

Passive Knowledge Capture

Beyond explicit /ingest and /find commands, quarry runs as a Claude Code plugin with hooks that capture knowledge automatically during your sessions:

Hook When it fires What it does
Session start On every session start Auto-registers your project directory and syncs it in the background. Your codebase is searchable without manual ingestion.
Web fetch After any WebFetch tool call URLs Claude fetches during research are auto-ingested into a web-captures collection. Reuses already-retrieved content when available, falls back to URL ingest otherwise.
Pre-compact Before context compaction Captures the conversation transcript into a session-notes collection. Discoveries that would be lost when the context window shrinks are preserved as searchable chunks.

All hooks are fail-open — failures are ignored and never block Claude Code. Each hook is individually toggleable via .punt-labs/quarry/config.md YAML frontmatter. See AGENTS.md for the full integration model.

How It Works

Quarry runs as a daemon. Claude Code sessions connect through mcp-proxy:

                    stdio                       wss:// (TLS)
Claude Code <-----------------> mcp-proxy <---------------------> quarry serve
             MCP JSON-RPC       (~5 MB Go)      pinned CA cert    (one daemon)

Without the proxy, every session spawns a separate Python process, each loading the embedding model into ~200 MB of RAM. With it, startup is instant and state is shared across all sessions. All connections use TLS with a self-signed CA — even on localhost.

quarry install downloads mcp-proxy (SHA256-verified, correct platform) and configures MCP clients.

Documentation

Architecture | Z Specification | Design | Agents | Changelog

Development

uv sync                        # install dependencies
make check                     # run all quality gates (lint, type, test)
make test                      # test suite only
make format                    # auto-format code
make docs                      # build LaTeX documents

License

MIT

README copy captured when this server was indexed; it may lag the repository. The original is authoritative.

Questions

About Quarry

How do I install Quarry?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is Quarry safe to use with an AI agent?

Its trust score is 64 out of 100 (good). It passes 2 of the 3 static security checks run so far; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Quarry still maintained?

Yes — the latest release is v3.4.0 (21 Sep 2026), and the last commit was 26 days ago. The repository has 0 stars.