Arcana

An agent-first data catalog that gives AI the context to write correct SQL on the first try.

Text-to-SQL accuracy improves dramatically when agents know which tables matter, what columns mean, and what business rules apply. Arcana builds a semantic knowledge layer over your data warehouse — indexing table schemas, column definitions, lineage graphs, and business documentation into a unified search index — then serves exactly the right context to any MCP-compatible agent (Claude Code, Cursor, Copilot, or any custom toolchain).

Written in Rust for single-binary deployment, sub-millisecond search latency, and zero runtime dependencies. Token-minimal by design — the recommender returns only the highest-relevance context within a configurable budget, so agents spend tokens on reasoning, not sifting through schema dumps. Adapter-based architecture means Arcana works with any warehouse (Snowflake, BigQuery, Databricks) and any modeling framework (dbt, LookML, raw DDL) — ship one adapter and the entire platform lights up. SQLite-backed for zero-ops local use; deploy as a shared HTTP/SSE server when the team is ready.

Features

Quick Start

Option 1: Build from source

git clone https://github.com/aidancorrell/arcana
cd arcana
cargo build --release

Option 2: Docker

docker compose up -d

This starts Arcana with HTTP/SSE transport on port 8477 — multiple engineers can share one instance.

Initialize and sync

# Create a minimal config (see below)
cp config/arcana.example.toml arcana.toml

# Initialize the metadata store
arcana --config arcana.toml init

# Sync from your dbt project
arcana --config arcana.toml sync --adapter dbt

# Embed all definitions for semantic search
arcana --config arcana.toml reembed

# Test it
arcana --config arcana.toml ask "monthly revenue by region"

Connect to Claude Code

Add to your Claude Code MCP config (~/.claude.json or project settings):

{
  "mcpServers": {
    "arcana": {
      "command": "/path/to/arcana",
      "args": ["--config", "/path/to/arcana.toml", "serve", "--stdio"]
    }
  }
}

Minimal Configuration

[database]
url = "sqlite://arcana.db"

[embeddings]
provider = "openai"
openai_api_key = "sk-..."  # or set OPENAI_API_KEY env var
openai_model = "text-embedding-3-small"
dimensions = 1536

[dbt]
project_path = "./my-dbt-project"

That's it. Snowflake, enrichment, sync, and MCP sections are all optional. See config/arcana.example.toml for the full reference.

CLI Commands

arcana init         Initialize the metadata store
arcana sync         Sync metadata from dbt and/or Snowflake
arcana ingest       Ingest Markdown documents
arcana reembed      Embed (or re-embed) all semantic definitions
arcana enrich       Generate LLM descriptions for undocumented entities
arcana ask          Semantic search from the command line
arcana dedup        Detect redundant/duplicate tables
arcana status       Show entity counts and definition coverage
arcana serve        Start MCP server (--stdio for local, HTTP for team)

MCP Tools

When connected as an MCP server, Arcana exposes:

Architecture

Arcana is a Rust workspace with 6 crates:

arcana-core          Metadata store, entity types, embedding index, confidence system
arcana-adapters      Snowflake + dbt metadata adapters
arcana-documents     Document ingestion pipeline (Markdown)
arcana-recommender   Context ranker + token-budget-aware serializer
arcana-mcp           MCP server (stdio + HTTP/SSE transports)
arcana-cli           CLI binary wiring everything together

See docs/ARCHITECTURE.md for the full design document.

Development

cargo test --workspace    # 30+ tests, no API keys needed
cargo check --workspace
cargo fmt --all
cargo clippy --workspace

Sandbox testing

cd sandbox && ./setup.sh  # Clones jaffle-shop, generates dbt artifacts, inits store

For scale testing with 3,400+ models, see sandbox/README.md.

License

MIT