Schema Linker

Agent skill: skills/SKILL.md

Spec: spec/schema_linking.md

Schema Linker discovers how tables relate in an existing database and emits a compact, LLM-ready Markdown report of declared PK/FK joins plus inferred join candidates with evidence labels. Useful for text-to-SQL, multi-table query authoring, and join-path debugging. Supports SQLite, PostgreSQL, MySQL, MariaDB, and DuckDB. Requires Python >= 3.10.

It writes one Markdown report per schema at <db_name>/<schema>_schema_links.md (for example app/main_schema_links.md), where <db_name> is the database or file name without its directory or extension. Use --output to change the directory.

AI agents and text-to-SQL pipelines can read this context instead of guessing join paths.

Quick Start

Install with pip:

pip install schema-linker

Or run instantly with uvx (no install needed):

uvx schema-linker --db-type mysql --user user --password password --database db --schema sch --port 3306

This creates a report at db/sch_schema_links.md.

The output directory defaults to the database name (<db_name>/). For SQLite/DuckDB the schema is main, so --database path/to/app.sqlite writes app/main_schema_links.md. Override the directory with --output. See What The Output Contains for what's inside.

What The Output Contains

The schema links .md file contains:

• Declared primary-key and foreign-key links from database constraints. Omitted by default to save tokens; include them with --show-declared-links.
• Inferred links from name, type, cardinality, and containment evidence.
• Evidence labels for each inferred join candidate. Omitted by default to save tokens; include them with --show-evidence.

Treat inferred links as candidates, not guaranteed joins. Validate them against the user question and the table data before writing final SQL.

How It Works

Declared links are read straight from the database's primary/foreign-key constraints. For everything not already covered by an FK, Schema Linker runs a cheap-to-expensive pipeline (full details in spec/schema_linking.md):

1. Metadata — skip FK-covered columns and pairs with mismatched data types.
2. Cardinality — estimate COUNT(DISTINCT col); drop near-unique free text, keep moderate, ID-like columns.
3. Name/type — match column names (Levenshtein-style similarity) to find strong candidates worth a spot-check.
4. Containment — extract distinct values only for the survivors, then use MinHash + LSH Ensemble to find one-way set containment (this handles unequal cardinalities, unlike Jaccard).
5. Verify — confirm each candidate with an exact containment check and require at least three independent pieces of evidence.

A link survives only when name, type, cardinality, and containment agree — which is why evidence is reported per signal.

Sample Output

This is the real report produced by the runnable example below (a tiny SQLite shop database). By default the Declared PK/FK Links section is omitted to save tokens:

# Schema Links

- version: 0.0.1
- dialect: sqlite
- database: examples/shop.sqlite
- schema: main

## Inferred Links

### customers.customer_id
- inferred: support_tickets.customer_id
- declared: orders.customer_id

Add --show-declared-links to prepend the declared section at the top:

## Declared PK/FK Links

order_lines.order_id -> orders.order_id
orders.customer_id -> customers.customer_id

• Declared PK/FK Links come straight from database constraints — the safe, guaranteed joins. Declared links are always used to group the inferred links below; the section itself is optional.
• Inferred Links are grouped by shared value domain. Under each anchor, inferred: lists new join candidates and declared: lists columns already covered by a foreign key (shown for context).

Here support_tickets.customer_id is flagged as a likely join onto customers.customer_id even though there is no FK constraint — exactly the case text-to-SQL agents usually have to guess.

Add --show-evidence to see the signal behind each candidate (real output from the same database):

### customers.customer_id
- inferred:
  - support_tickets.customer_id: minhash containment candidate, moderate ID-like cardinality, name match, shared name tokens, similar names, type match
- declared: orders.customer_id

Evidence labels report the why (name match, table-name id match, shared name tokens, similar names, type match, minhash containment candidate) and the confidence (same distinct count, similar distinct counts, low cardinality, moderate cardinality, moderate ID-like cardinality).

Runnable Example

Reproduce the output above against a seedable SQLite database in examples/:

# 1. build examples/shop.sqlite (customers, orders, order_lines, support_tickets)
python examples/seed_shop.py

# 2. link it; --output examples writes examples/main_schema_links.md
schema-linker --db-type sqlite --database examples/shop.sqlite --output examples

cat examples/main_schema_links.md

The fixture deliberately leaves support_tickets.customer_id without a foreign key, so inference — not the catalog — surfaces that join. Re-run with schema-linker ... --show-evidence to see the evidence labels above.

Database Examples

SQLite

schema-linker --db-type sqlite --database path/to/app.sqlite

PostgreSQL

schema-linker --db-type postgres --database app_db --schema sch --user readonly_user --host localhost --port 5432 --ask-password

MySQL

schema-linker --db-type mysql --database app_db --user readonly_user --host localhost --port 3306 --ask-password

MariaDB

schema-linker --db-type mariadb --database app_db --user readonly_user --host localhost --port 3306 --ask-password

DuckDB

schema-linker --db-type duckdb --database warehouse.duckdb --schema sch

Environment Variables

Connection values can come from environment variables instead of flags:

SCHEMA_LINKER_DB_TYPE=sqlite \
SCHEMA_LINKER_DATABASE=path/to/app.sqlite \
schema-linker

Supported variables:

• SCHEMA_LINKER_DB_TYPE
• SCHEMA_LINKER_DATABASE
• SCHEMA_LINKER_DB_HOST
• SCHEMA_LINKER_DB_PORT
• SCHEMA_LINKER_DB_USER
• SCHEMA_LINKER_DB_PASSWORD
• SCHEMA_LINKER_SCHEMA

For server databases, --host defaults to localhost, --port defaults to the database default, and Schema Linker securely prompts for a password when SCHEMA_LINKER_DB_PASSWORD is not set.

Help

schema-linker -h

Table filters:

schema-linker --db-type sqlite --database app.sqlite --include-tables users,orders,line_items
schema-linker --db-type sqlite --database app.sqlite --exclude-tables audit_log,temp_imports

Schema filter:

schema-linker --db-type postgres --database app_db --schema reporting --user readonly_user --port 5432 --ask-password

Options:

• --include-tables table_a,table_b: only inspect selected tables.
• --exclude-tables table_c: skip selected tables.
• --include-technical-tables: link migration/framework tables that are skipped by default.
• --containment-threshold 0.8: minimum exact containment for inferred links.
• --max-distinct-values 10000: maximum distinct values loaded per candidate column.
• --show-evidence: include evidence labels on inferred links (off by default to save tokens).
• --show-declared-links: include the Declared PK/FK Links section (off by default to save tokens; declared links are still used to group inferred links).

Python API

Use the lower-level API when you already have a SQLAlchemy engine or need options:

from sqlalchemy import create_engine
from schema_linker import SchemaLinkOptions, link_schema

engine = create_engine("sqlite:///path/to/app.sqlite")

schema_links_md = link_schema(
    engine,
    SchemaLinkOptions(containment_threshold=0.9),
)

License

The Schema Linker source code is licensed under the MIT License. See LICENCE.

Third-party Python dependencies remain under their own upstream licenses. See THIRD_PARTY_NOTICES.md for a dependency license summary.