schemagate — text-to-SQL access control at schema selection

Your text-to-SQL agent picks which tables to show the model before anyone checks what the caller is allowed to read. schemagate does the check first: it filters the schema by the caller's grants, so restricted tables are absent from the prompt rather than ranked low. Works with LangChain, MCP, or any SQL agent, on Postgres, Oracle, MySQL, SQL Server and SQLite.

With row-level security alone the failure is quiet: the model writes valid SQL against a table the caller cannot read, RLS strips every row, and the user is told "no records found" — indistinguishable from "this data does not exist."

Demo · Install · Benchmarks · Local models · What it costs · Coming from Vanna

Same question, two callers, no database and no key:

schemagate demo "salary by employee"                                     # hr_compensation absent
schemagate demo "salary by employee" --principal okta:hr --role payroll  # now it is first

Absent, not ranked low. A table the caller may not read never enters the
prompt, so no rewording of the question reaches it and there is nothing to
filter out of the answer afterwards.

Try it in the browser — no
install, no database, no model call.

And it answers

The selection is a prompt, so the rest follows:

pip install schemagate
schemagate demo "which customers owe us money" --answer --provider anthropic --model <model-id>

main.crm_customer   main.crm_contact   main.v_customer_balance   (+5)
8 of 42 objects  ·  ~383 prompt tokens instead of ~2,036

-- SQL written by Anthropic / claude-sonnet-5, from 8 tables
SELECT c.id, p.display_name, v.account_number, v.invoiced, v.paid,
       (v.invoiced - v.paid) AS balance_due
FROM v_customer_balance v
JOIN crm_customer c ON c.id = v.id_customer
JOIN core_party  p ON p.id = c.id_party
WHERE v.invoiced > v.paid

id  display_name       account_number  invoiced  paid     balance_due
--  -----------------  --------------  --------  -------  -----------
1   Northwind Trading  ACC-1001        33960.0   22080.0  11880.0
2   Kellner GmbH       ACC-1002        8760.0    3000.0   5760.0

Rows, from a question, with no database to set up — that runs against a
bundled 42-object schema. Point it at your own with --url:

schemagate select "which customers owe us money" \
  --url "postgresql+psycopg://user:pw@host/db" --answer --provider anthropic --model <model-id>

No key? Drop --provider and it prints a prompt to paste into any chat, then
run the SQL it gives you back with --sql "SELECT ...".

More of the bundled schema, with the questions people actually type:

schemagate demo "which customers owe us money"
schemagate demo "late shipments by carrier" --prompt   # the DDL the model gets

Against your own database it's the same shape:

schemagate select "revenue by month" --url postgresql://localhost/app --principal okta:jdoe --role finance
schemagate studio --url postgresql://localhost/app        # the same thing, as a page

schemagate studio opens a local page where you type questions, switch the caller's
roles, edit hints, and watch what reaches the prompt and what doesn't. The same
page runs publicly at https://ashishsinha1602.github.io/schemagate/ on the six
bundled schemas, in your browser, with no server behind it. The selector on that page is a JavaScript
port of this library, and a test runs both against 1,789 cases and requires
identical rankings.

If you're coming from Vanna (archived March 2026), docs/migrating-from-vanna.md
is the short version: Vanna applied identity when the SQL ran; schemagate applies
it before the model sees the schema. Your User maps to a Principal in one
line.

What it saves

Every text-to-SQL call pays for the schema in the prompt. Dump the whole thing
and you pay for every table on every question; hand the model six tables and
you pay for six. Measured on the test schemas, average over their golden
questions, same built-in estimator as tests/bench.py:

schema	objects	full schema, every call	schemagate, average	reduction
Commerce	42	2,483 tokens	604	76%
Clinical claims	27	1,568	543	65%
Claims warehouse (star)	51	3,312	880	73%
Bank ledger and trading	39	2,255	637	72%
IoT telemetry	40	2,125	448	79%
Hostile (4 schemas, copies of everything)	260	16,095	444	97%

The last row is the one that matters: the selection stays around six tables
no matter how big the schema is, so the saving grows with the schema. Real
databases are the last row, not the first.

Worked example, with a price you should replace with your own: a 260-object
schema, 5,000 questions a day, an input price of $3 per million tokens. Full
schema: 16,095 × 5,000 × 30 = 2.4 billion tokens a month, about $7,200. With
schemagate: 444 × 5,000 × 30 = 67 million, about $200. The
browser demo has these two
numbers as editable fields under the stats, so you can put in your own volume
and price and watch it recompute against whatever question you ask.

Two more things that cost nothing here and money elsewhere: the selector
itself never calls a model (BM25 plus a hashed embedder, offline,
milliseconds), and the optional descriptions can be written by any chat window
you already pay for instead of an API key — see
Without an API key.

The problem this solves

Two things go wrong when you point an LLM at a database schema.

The first is cost. Most systems paste the whole schema into the prompt on every
question. That's fine for twenty tables and ruinous for two thousand.

The second is worse, and it's the reason I wrote this. Schema selection happens
before the query runs, so it happens before row-level security can do
anything. If your selection step isn't identity-aware, the model gets handed a
table the caller can't read. It writes perfectly good SQL. RLS or VPD filters
every row out. The user sees "no records found" and believes it.

That's not an access-denied message. It's a wrong answer with a confident tone,
and the user has no way to tell the difference. Filtering the catalog by
identity first is the only way I know to avoid it.

from schemagate import Catalog, Principal

cat = Catalog().bootstrap("postgresql://localhost/app")
cat.hint("invoice_draft", "pre-issue drafts only, not real revenue")
cat.restrict("hr_compensation", ["payroll"])

sel = cat.select("revenue by month", top_k=6,
                 principal=Principal("okta:jdoe", roles={"finance"}))

sel.prompt_fragment()   # compact DDL, ready for the system prompt
sel.object_list         # [{'owner': ..., 'name': ...}]
sel.explain()           # why each object was picked

hr_compensation is not in that result and its name does not appear anywhere
in the prompt text.

Install

pip install schemagate

That's the whole thing. One dependency (SQLAlchemy), no API key, no model
download. The default embedder is a hashed n-gram vectoriser that runs offline
and gives byte-identical results on every machine.

Extras, all optional:

pip install 'schemagate[postgres]'     'schemagate[oracle]'
pip install 'schemagate[mssql]'        'schemagate[mysql]'
pip install 'schemagate[anthropic]'    'schemagate[openai]'      'schemagate[gemini]'
pip install 'schemagate[huggingface]'

huggingface is the no-key, nothing-leaves-the-machine path, and it is the
one extra that is heavy: about 2 GB of wheels plus a 3.1 GB model download the
first time you use it. It is deliberately kept out of schemagate[all].
docs/local-models.md has the whole story — the
downloads, the load you wait through once, what it is good at and where it is worse
than a hosted model.

Quick start

pip install schemagate            # add an extra for your driver, below
schemagate studio                 # opens http://127.0.0.1:8770

Or without installing anything, with every driver already in the image:

docker run -p 8770:8770 -e SCHEMAGATE_DATABASE_URL=postgresql://…   ghcr.io/ashishsinha1602/schemagate

Leave the URL off and it opens on a 42-object sample schema with data in it,
so there is something to ask questions of before you point it at your own.

Then, in the page:

1. Connect. Paste a URL — postgres://…, postgresql://…, mysql://…,
   oracle://… and a JDBC string all work, as does the wallet form for an
   Autonomous Database. Tick Save this connection and give it a name and
   the next start reconnects on its own.
2. Catalogue. Settings → Model → pick a provider, paste a key, Save
   model (it is saved, so a restart does not ask again). Then Catalogue
   this database in the rail. One sentence per object, cached to disk, so a
   second run costs nothing.
3. Ask. Type a question in your own words. You get the objects that answer
   it, the DDL a model would receive, the SQL, and the rows.

Drivers come as extras — schemagate[postgres], [oracle], [mysql],
[mssql], or schemagate[all] for the lot:

pip install 'schemagate[postgres]'

When a question picks the wrong table

Two levers, both per database and both applied on every reconnect:

• Hints (rail → Hints): one object, in your words. "MyConvo campaigns:
  personal-inbox sends from a user's own mailbox."
• Glossary (POST /api/glossary): one word, everywhere. A term here is
  fed to the cataloguing prompt, so every description uses your vocabulary,
  and expanded into questions that mention it.

A hint beats a generated description everywhere, and neither needs
re-cataloguing.

Commands

Every subcommand, and what it is for. schemagate <command> --help prints the
same thing.

schemagate demo      [question]          run against the bundled 42-object schema
schemagate select    --url URL [question]  select against your own database
schemagate studio    [--url URL]         the Studio page, served locally
schemagate describe  --url URL           write AI descriptions for your objects
schemagate certify   URL                 end-to-end check on a real engine

demo and select

select is demo pointed at a real database; they take the same flags.

schemagate demo "who reports to whom"
schemagate select --url postgresql+psycopg://user:pw@host/db "unpaid invoices"

flag	what it does
--top-k N	how many objects to select (default 6)
--principal SOURCE:ID	who is asking, e.g. okta:jdoe, db:APPUSER. Must be namespaced
--role ROLE	a role the caller holds; repeatable
--prompt	print the prompt instead of the selection
--explain	show why each object was picked, and what was withheld
--answer	write the SQL and run it (needs a provider)
--provider NAME / --model ID	which model to use
--limit N	row cap for --answer
--restrict-from-grants	take visibility from the database's own GRANTs
--rerank	let the model reorder the shortlist the maths produced
--values	sample short, non-personal column values
--include / --exclude PATTERN	narrow what is reflected (select only)
--schema NAME, --no-fk, --config JSON, --sql SELECT	select only

studio

schemagate studio                       # empty, connect from the page
schemagate studio --demo                # the bundled sample schema
schemagate studio --url postgresql://localhost/app
schemagate studio --remember            # save the connection, reconnect next time
schemagate studio --forget              # delete the saved connection and exit

flag	what it does
--url URL	connect at startup instead of from the page
--host / --port	default 127.0.0.1:8770
--no-browser	do not open a browser
--demo	open on the bundled sample schema
--remember	save this connection to ~/.schemagate/connection.json (0600) and replay it on the next start. Includes the database and wallet passwords, so it is off unless asked for
--forget	delete that file and exit
--allow-connect / --no-connect	whether the page may open a database itself. On by default on loopback, off when bound anywhere else
--restrict-from-grants	derive visibility from GRANTs at startup
--values	sample column values while reflecting
--include / --exclude / --config	as for select

In the page: Catalogue this database describes what has no description yet,
Re-catalogue all rewrites every one, and Resync schema re-reflects the
database while keeping the descriptions you already have.

describe

# with a key
schemagate describe --url postgresql://localhost/app --provider anthropic --model claude-sonnet-5

# without one: write the prompt out, paste it into any chat, apply the reply
schemagate describe --url postgresql://localhost/app --out prompt.txt
schemagate describe --url postgresql://localhost/app --apply reply.json

flag	what it does
--out FILE	write the prompt instead of calling a model
--apply REPLY.json	apply a reply produced that way
--all	re-describe everything, not only what is missing
--cache FILE	where to keep generated descriptions; re-runs are then free
--provider / --model	which model to use
--include / --exclude / --schema / --config	as for select

certify

schemagate certify "postgresql+psycopg://user:pw@host/db"

Reflects, selects, and reports what a real engine actually did — the check to
run before trusting a new database or driver.

Environment

variable	what it does
SCHEMAGATE_CONNECT_ARGS	JSON passed to create_engine(connect_args=...), for connections a URL cannot express (an Autonomous Database wallet)
SCHEMAGATE_REMEMBER=1	save the connection without passing --remember
SCHEMAGATE_HOME	where connection.json lives (default ~/.schemagate)
ANTHROPIC_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY / GOOGLE_API_KEY, OCI_COMPARTMENT_ID	picked up automatically by --provider
SCHEMAGATE_STUDIO_LOG=1	log Studio requests

How it picks

1. Reflect the schema through SQLAlchemy. No vendor SQL anywhere.
2. Index names, columns, comments, hints, and view definitions. That last one
   matters more than it sounds: a view exposes only its output columns, so
   v_stock_shortfall looks like it's about "shortfall" when the thing you'd
   search for, reorder_point, is buried in its SELECT.
3. Retrieve with reciprocal-rank fusion over BM25 and vector similarity.
   Neither alone is good enough. Vectors miss exact identifiers; BM25 misses
   "owe us money" → balance.
4. Walk foreign keys to pull in join tables the question never mentions. In my
   experience this is the single biggest cause of generated SQL that parses
   but won't run.
5. Apply the caller's identity at every step above.

Numbers

On public benchmarks, so you can check them without trusting me:
BENCHMARKS.md has schemagate on Spider and BIRD, with the
scripts in benchmarks/ and the data downloaded from the
original sources. The headline is the pooled setting — every Spider database
merged into one 876-table catalog, no hint about which one to look in:

Spider dev, 876 tables pooled	all gold tables present
top_k=5	71.4%
top_k=10	82.6%
top_k=20	92.9%

BIRD dev, 1,534 questions: 96.5% per-database at top_k=5, 91.1% pooled at
top_k=10.

And Spider 2.0-lite, the benchmark built for real warehouses — 162
databases, 8,255 tables, a median of 16 per database and a maximum of 785:
79.8% at top_k=10 over all 247 usable questions, none excluded (84.5% on
the 233 whose gold tables resolve), no pooling needed because the databases
are already big. An earlier version of this page said 82.9%; that number was
measured while Windows had silently made thousands of the schema files
unreadable, and a second re-run was owed after the loader was found to be
reading the benchmark's per-column descriptions as one table description.
Both re-runs are done and BENCHMARKS.md keeps the
corrections rather than deleting them.

End to end, on the metric those boards actually score: schemagate plus
claude-opus-5 gets 68.0% execution accuracy on BIRD dev (102/150, seeded
sample), with 97.3% of questions producing SQL that runs. The published GPT-4
baseline on BIRD dev is around 46%.

None of these is a leaderboard placing — that needs the held-out test set, and
nothing here has been submitted.

Everything below is measured on schemas I invented, which is worth less and
is why the public numbers come first.

Six test schemas ship with the library. Run python tests/bench.py and you
get all of this printed back. TESTING.md is the full record of what was
tested, what broke, and what was found to be the database rather than schemagate.

Every number below is printed by that run, and the run fails if any of them
stops matching — bench.py reads this table back and compares.

recall@6 here is the share of gold tables retrieved in the top six,
micro-averaged over questions. The literal column asks questions that
reuse the schema's own vocabulary; the business words column asks for the
same things the way a person does, with no vocabulary overlap. Both matter and
they disagree, which is the point of showing both.

schema	objects	recall@6, literal	recall@6, business words
commerce	42	100%	50.0%
clinical claims	27	100%	46.7%
claims warehouse (star)	51	100%	50.0%
bank ledger and trading	39	100%	64.3%
IoT telemetry	40	100%	60.0%
hostile (4 schemas, copies of everything)	260	100%	85.7%

real table beats its backup/staging copy, 19 cases across schemas	19/19
recall without foreign-key expansion	93.8%
prompt tokens, full schema every call	2,812
prompt tokens, schemagate average	764 (−72.8%)

Measured with the hashed embedder — what pip install schemagate gives
you, no extras. schemagate[huggingface] swaps in sentence-transformers and
the business-word numbers move a long way: on the held-out paraphrase set
tests/run_paraphrase_eval.py reports 58.6% overall hashed and 82.8% with
MiniLM. That harness counts a question as hit if any gold table is
retrieved, which is a looser predicate than this table's, so its figures are
not comparable with these — it prints which embedder it used for the same
reason.

Token counts come from an estimator built into the benchmark so the number is
reproducible with no network and no extra install. pip install tiktoken and
the same script switches to exact cl100k_base counts. The ratio holds either
way.

Six schemas rather than one because a single schema whose questions happen to
share vocabulary with its own table names will flatter any retriever. The
second is a different domain entirely. The third is 260 objects of deliberate
sabotage: an _archive and _stg copy of every table, the same table name in
three schemas, an 8-deep foreign-key chain, a reference cycle, composite keys,
a 320-column table, 100-character identifiers, and names in Spanish and
Japanese. The fourth is a claims warehouse star schema built so that several
tables are plausible for every question and one is right: the same fact at
four grains, a slowly-changing member dimension with a history table, one date
dimension joined five different ways, bridge tables, and fifteen _bkp,
_old, _v2, _tmp and stg_ copies of the important ones. The fifth is a
bank: a ledger at three grains, trades versus positions versus settlements,
FX both as a daily table and an as-of view, lending, and the KYC and AML
tables most callers must never see. The sixth is an IoT fleet: readings at
raw, one-minute and hourly grains, six monthly partition tables, an alarm
lifecycle spread across three tables. All six are invented. No real schema
from anywhere is in this repo.

That 50% row is the honest one. Read it before you adopt this.

The 50% row, and what to do about it

The default embedder matches subwords, not meaning. Ask it for "things we're
running out of" and it will not find v_stock_shortfall, because those two
strings have nothing in common. Ask it about stock_shortfall and it's
excellent.

If your users type identifier-shaped questions, you're done, and you never need
an API key. If they type like people, give the catalog descriptions. There are
two ways, and neither is required.

With an API key in the environment, you get them without asking. Every path
that answers a question — schemagate select, --answer, the MCP server, the
Studio's connect — describes the catalogue first, caches the result per
connection under ~/.schemagate/descriptions/, and re-describes an object only
when its structure changes. A hint you wrote, or a database comment that says
something, is never overwritten; a comment that only restates the object's
name in the schema's own boilerplate is replaced, because it was diluting
every word it contained. Measured on a 1,200-object schema, that is the
difference between six and eight of eight complex questions producing SQL that
runs. SCHEMAGATE_AUTO_DESCRIBE=0 turns it off; with no key present nothing
is called and nothing changes.

Without an API key

Any chat window you already have — ChatGPT, Gemini, Copilot, a
local model — can write the descriptions. schemagate gives you the prompt and
takes the reply:

schemagate describe --url postgresql://localhost/app --out prompt.txt
# paste prompt.txt into a chat; save its JSON reply as reply.json
schemagate describe --url postgresql://localhost/app --apply reply.json --config catalog.json
schemagate select   --url postgresql://localhost/app "things we're running out of" --config catalog.json

The prompt is metadata only — names, types, comments, foreign keys, never rows
— and one paste covers every undescribed object. The reply lands in the
describe block of catalog.json, next to your restrict and hint blocks,
and select, studio and the MCP server (SCHEMAGATE_CATALOG_CONFIG) all
read it. From Python it's the same idea: cat.describe_prompt() and
cat.describe({"v_stock_shortfall": "Items below their reorder level."}).

With a local model, and no key at all

pip install 'schemagate[huggingface]'
schemagate describe --url postgresql://localhost/app                     --provider local --model Qwen/Qwen2.5-1.5B-Instruct                     --cache .schemagate-cache.json

or, in the Studio, Settings → Model → Local (transformers). No key
field appears, because there is no key.

from schemagate.ai import SchemaDescriber, LocalProvider
cat.describe(SchemaDescriber(LocalProvider(), cache_path=".schemagate-cache.json"))

Writing one sentence per table is a small enough job that a 1.5B model does it
acceptably. Writing multi-table SQL is not, and the Studio uses the same
provider for both — so if you have a key, catalogue locally but answer with
the key. Read docs/local-models.md before you turn it
on: it covers the two downloads, the load you wait through once, the ~3 GB of RAM, the
caching that makes the second run free, and why a weak model's bad description
can no longer bury the object it describes.

With your own key

from schemagate.ai import SchemaDescriber, AnthropicProvider

cat.describe(SchemaDescriber(AnthropicProvider(model="claude-sonnet-4-5"),
                             cache_path=".schemagate-cache.json"))

One sentence per table, written by the model, indexed like any other schema
text. On the bundled schema that takes the business-words row from 50% to 100%
with no change to the identifier-style questions.

Anthropic, OpenAI and Gemini are supported. Anything else goes through
CallableProvider, which is also your escape hatch when a vendor changes their
SDK and you don't want to wait for a release from me.

from schemagate.ai import (AnthropicProvider, OpenAIProvider, GeminiProvider,
                      CallableProvider, auto_provider, available_providers)

AnthropicProvider(model="claude-sonnet-4-5")                    # ANTHROPIC_API_KEY
OpenAIProvider(model="gpt-4.1-mini")                            # OPENAI_API_KEY
GeminiProvider(model="gemini-2.5-flash")                        # GEMINI_API_KEY
OpenAIProvider(model="…", base_url="http://localhost:11434/v1") # anything local
CallableProvider(lambda system, prompt: my_llm(system, prompt))

available_providers()      # ['AnthropicProvider'] — names, never key values
auto_provider(model="…")   # picks whichever key is set

model is required. I'm not shipping a default model ID, because model IDs
change every few months and a hardcoded one eventually 404s for everybody who
installed the version before the fix.

Three things worth knowing before you turn this on:

What leaves your network. Table names, column names, types, nullability,
existing comments, foreign keys. Not one row of data — ObjectDoc has no field
that could hold one, and there are tests asserting both halves of that. Nothing
is sent unless you call describe().

What it costs. One short call per undescribed object, once. Objects that
already have a database comment or a hint are skipped by default. Results cache
by content, so re-running is free and only changed tables get re-described. Ask
before you pay:

describer.estimate_calls(docs)   # calls describe() would actually bill for
describer.preview(doc)           # the exact text that would be sent

What happens when it fails. The object is skipped, cataloging continues, and
describer.failures lists what was missed. Pass strict=True if you'd rather
it raise. A hint() you wrote by hand always beats a generated description, so
fixing a bad one costs nothing.

The embedder picks itself

pip install schemagate uses the hashed n-gram vectoriser: offline, instant,
byte-identical on every machine. Install schemagate[huggingface] and a
sentence model is used automatically instead -- no flag, no benchmark, no
decision for you. Measured across the six bundled schemas, 98 questions, no
descriptions:

	recall@6
hashed n-gram (base install)	90/98
all-MiniLM-L6-v2 ([huggingface])	93/98

The gain is concentrated exactly where the hashed embedder is documented to be
weak -- questions phrased the way people speak. On the commerce schema, which
carries that set, it goes 15/18 to 18/18.

It is not the base default because that would trade one dependency for torch,
and the guarantee that the same text gives the same vector everywhere.
SCHEMAGATE_AUTO_EMBEDDER=0 keeps the hashed one if you need to reproduce an
older index.

You can swap in a hosted embedder too, though on identifier-heavy schema text
the offline ones are often just as good and cost nothing per query.

from schemagate.ai import APIEmbedder, OpenAIProvider

provider = OpenAIProvider(model="gpt-4.1-mini",
                          embed_model="text-embedding-3-small")
cat = Catalog(embedder=APIEmbedder(provider, dim=1536))

Connecting to what you actually have

Most people do not have a SQLAlchemy URL. They have a wallet zip, a JDBC
string out of a config file, or a host and a port. schemagate.connect
turns any of those into the two things SQLAlchemy needs:

from sqlalchemy import create_engine
from schemagate import Catalog
from schemagate.connect import resolve

url, connect_args = resolve("jdbc:oracle:thin:@//host:1521/ORCLPDB1")
cat = Catalog().bootstrap(create_engine(url, connect_args=connect_args))

connect_args is not optional. An Autonomous Database has no URL worth the
name — the wallet directory, the wallet password and the TNS alias have
nowhere to live in one — so the URL degenerates to oracle+oracledb://@ and
the connection travels beside it:

url, connect_args = resolve({
    "kind": "wallet",
    "wallet": "~/Downloads/Wallet_mydb.zip",   # the zip as downloaded
    "alias": "mydb_high",
    "user": "ADMIN", "password": "...",
})

The zip is extracted next to itself, because the driver re-reads it on every
reconnect — a temporary directory gives you a connection that works once.

Same thing from the Studio, with a dropdown instead of a dict:

pip install "schemagate[all]"          # every driver, the model SDKs, MCP
schemagate

On Oracle Cloud, add the OCI SDK so cataloguing can go through OCI Generative
AI with no API key at all:

pip install "schemagate[all,oci]"

It is a separate word because it is a separate size: the OCI SDK is 488 MB and
17,505 modules, against 217 MB for everything else together.

you have	pick
postgresql+psycopg://...	SQLAlchemy URL
jdbc:oracle:thin:@//host:1521/SVC	JDBC URL
Wallet_mydb.zip + mydb_high	Oracle wallet
a host, a port and a database	the engine by name

Once connected, the Studio shows the command that reproduces it — the
schemagate select ... --url ... line and the Python equivalent, with the
password as $DB_PASSWORD rather than the real one. Try it in the page, then
take the command.

Connecting works out of the box on localhost, where the only person who can
reach the page is already sitting at a shell on that machine. Serve the Studio
on any other address and it takes --allow-connect, because there it becomes
a URL box anyone on the network can use to make your server connect to hosts
only it can see. --no-connect turns it off anywhere. A failed connection reports the
exception type and nothing else, because driver errors quote the URL they
were given and a URL carries a password.

Restricting one column, and reading the ACL you already have

An object-level rule cannot express the common case: the table is the right
answer and one column in it is not.

from schemagate import Catalog, Principal

cat = Catalog().bootstrap("postgresql+psycopg://user:pw@host/db")
cat.restrict_column("employee", "salary", ["payroll"])
cat.index()

analyst = Principal("okta:jdoe")
print(cat.select("who reports to whom", principal=analyst).prompt_fragment())

salary is absent from that fragment — not REDACTED, not renamed. The
name is itself the disclosure: a model that knows the column exists can ask
about it, join on it, or mention it in an explanation. Any -- FK line naming
a withheld column is dropped too, since it would put the identifier straight
back.

What was shown, and to whom

sel = cat.select("who reports to whom", principal=analyst)
sel.to_dict()
# {'question': 'who reports to whom',
#  'principal': 'okta:jdoe', 'roles': [], 'total_objects': 219,
#  'hits': [{'object': 'hr.employee', 'kind': 'TABLE', 'score': 0.031,
#            'reason': 'hybrid', 'columns_shown': 3, 'columns_withheld': 1}]}

The record an auditor asks for after the fact, and the one thing that cannot
be reconstructed later — the catalog will have changed, roles will have
changed, and the question is gone. It carries the count of withheld
columns, never their names: a log that lists what it withheld has disclosed it
to everyone who can read the log.

Deriving visibility from GRANTs

At forty tables a hand-written restrict map is fine. At four hundred it is a
second copy of an ACL that already exists in the database, and two copies
drift.

schemagate select "what do we pay our doctors" \
  --url "postgresql+psycopg://user:pw@host/db" --restrict-from-grants

from schemagate.grants import restrict_from_grants
report = restrict_from_grants(cat, engine, report=True)
print(report)
# postgresql: 629 object(s) seen, 218 restricted, 1 public, 0 unmatched, 1 role(s) expanded

PostgreSQL and Oracle. Nested roles are flattened transitively, so a user
whose group maps to a role that inherits the granted one still reaches the
object. An object with no grant row is left untouched and named in
report.objects_unmatched — silence is not a denial, and restricting on
absence would break a working catalog the first time a connection could not
see everything.

It reads grants, so it does not see row-level policies. Measured on Oracle
26ai: a caller whose Virtual Private Database policy admits zero rows still
holds SELECT in ALL_TAB_PRIVS, still appears in ALL_TABLES, and is still
put in front of the model with every column. Nothing leaks -- the database
enforces the policy -- but the prompt names a table that caller cannot get a
row out of. docs/row-level-security.md has the
measurement, what to do about it today, and the fix.

Databases

Reflection uses only SQLAlchemy's dialect-agnostic Inspector. There's no
hand-written SQL in schemagate.introspect and a test fails the build if any
appears, so in principle any dialect SQLAlchemy supports will work.

In principle isn't evidence, so there's a script:

python scripts/certify_dialect.py 'postgresql+psycopg://user:pw@host/db'
python scripts/certify_dialect.py 'oracle+oracledb://user:pw@host:1521/?service_name=FREEPDB1'
python scripts/certify_dialect.py 'mssql+pyodbc://user:pw@host/db?driver=ODBC+Driver+18+for+SQL+Server'
python scripts/certify_dialect.py 'mysql+pymysql://user:pw@host/db'

It creates three schemagate_cert_ tables, reflects them, runs selection and
identity scoping end to end, drops them again, and exits non-zero if anything
failed. Point it at a scratch schema.

SQLite	certified, 10/10, in CI
PostgreSQL	certified, 10/10 on PostgreSQL 16, plus the full 260-object suite
Oracle	certified live on Oracle AI Database 26ai (Autonomous Database), Sep 2026: certify script 10/10, the native VECTOR(512, FLOAT32) store conformance suite, and the dialect suite. Also stress-tested against a 127-object, 3-domain schema with ~7M rows
SQL Server	certified live on SQL Server 2022 (16.0.4295.3), 10/10, in CI on every push against a service container, plus 13 live dialect tests covering alias types, hierarchyid/sql_variant, and max_length being bytes
MySQL / MariaDB	certified live on MySQL 8.4.11, 10/10, in CI on every push against a service container, plus the GRANT reader suite: all three privilege levels, the role graph, and the role-only blind spot MySQL cannot report. MariaDB has not been run

Every row above has a real database behind it. The one thing still worth
saying plainly: MariaDB is inferred from MySQL rather than run. Point the
script at one and tell me what happens.

The same checks run under pytest if you export a URL, which is how CI certifies
a dialect for good:

export SCHEMAGATE_POSTGRES_URL='postgresql+psycopg://…'
export SCHEMAGATE_ORACLE_URL='oracle+oracledb://…'
export SCHEMAGATE_MSSQL_URL='mssql+pyodbc://…'
export SCHEMAGATE_MYSQL_URL='mysql+pymysql://…'
pytest tests/test_dialects.py -v

Using it from an agent

The MCP server trusts the identity it is handed. principal and roles
come from the client and are not authenticated -- there is no token and no
session. Anyone who can reach the transport can claim a role and read what
that role may read, and since run_query returns rows, that is data, not
just schema. Run it over stdio (the caller is your own desktop client), or
over HTTP behind something that authenticates the user and sets the
principal for them. It is a scoping mechanism, not a lock.

If you already have an agent that writes SQL, the fastest way in is to let it
call schemagate as a tool rather than wiring the library into your code.

MCP. Cursor, Windsurf, Zed, or anything else that speaks the
Model Context Protocol:

pip install 'schemagate[mcp]'
SCHEMAGATE_DATABASE_URL=postgresql://localhost/app python -m schemagate.mcp_server

MCP client config:

{"mcpServers": {"schemagate": {
  "command": "python", "args": ["-m", "schemagate.mcp_server"],
  "env": {"SCHEMAGATE_DATABASE_URL": "postgresql://localhost/app"}}}}

Three tools: select_schema (the DDL for a question, scoped to the caller),
list_objects (what this caller can see), describe_object (one object's full
DDL). All three take principal and roles. If the client leaves them out,
the caller is anonymous and sees only unrestricted objects. A restricted
object and a missing one return the same error, so existence doesn't leak.
SCHEMAGATE_DATABASE_URL=demo serves the bundled schema.

To host it for a team rather than one desktop:

SCHEMAGATE_MCP_TRANSPORT=streamable-http SCHEMAGATE_MCP_PORT=8765 python -m schemagate.mcp_server

It's built not to die. The index lives in memory after startup, so the
database going away does not take the server with it — select_schema keeps
answering from the last good reflection, and refresh_catalog reports the
failure instead of raising. Every tool catches everything and returns
{"error": ...}; a bad request cannot end the session for other clients.
health tells a load balancer what state it's in. A test throws 125 kinds of
garbage at every tool and then checks the next good request still works, and
another does the same through a real client over stdio. Works on MCP SDK 1.x
and 2.x; the 2.0 rename broke a fresh install once and there's a shim and a
test for it now.

LangChain. A proper BaseRetriever, so it composes:

pip install 'schemagate[langchain]'

from schemagate.integrations.langchain import SchemagateRetriever, prompt_fragment

retriever = SchemagateRetriever(catalog=cat, top_k=6,
                           principal=Principal("okta:jdoe", roles={"finance"}))
chain = retriever | RunnableLambda(prompt_fragment) | your_sql_prompt | llm

The principal is bound at construction on purpose. Build one retriever per
caller; a chain can't forget to pass identity if the retriever already has it.

On Oracle Cloud

Certified live on Oracle AI Database 26ai. Two ways in, neither of which needs
an API key — cataloguing runs on OCI Generative AI under your own OCI identity,
so the prompts (schema metadata only, never rows) stay in your tenancy.

From Cloud Shell, about a minute, no VM:

pip install --user 'schemagate[oracle,oci]'
schemagate describe --url 'oracle+oracledb://@' --provider oci \
    --model google.gemini-2.5-pro --config catalog.json

Or one click, for an MCP endpoint that stays up for your team:

That opens Resource Manager in your own tenancy with the stack loaded — an
Always-Free-eligible VM running the MCP server against an Autonomous Database
it creates, or one you already have. Details and the Terraform: oci/.

Keeping the index in Oracle

MemoryStore rebuilds on every process start. Fine for a few hundred objects,
wrong for a long-lived service. OracleStore keeps vectors in Oracle 23ai's
native VECTOR type so the nearest-neighbour search runs in the database:

from schemagate.stores.oracle import OracleStore

store = OracleStore(dsn="user/pw@host:1521/FREEPDB1", dim=512)
store.create_schema()                       # idempotent

cat = Catalog(store=store).bootstrap("oracle+oracledb://…")

Pass connection= instead of dsn= to reuse your app's pool. It won't close a
connection it didn't open.

Scoping is a predicate inside the scored subquery, not a filter applied after
the rows come back. A row the caller can't see is never ranked and never leaves
the database.

Same caveat as above: 26 tests pin the SQL, the bind types and the scope
predicate, and every statement is checked against an independent Oracle parser,
but none of it has run against a live 23ai instance yet. To do that:

export SCHEMAGATE_ORACLE_DSN='user/password@host:1521/FREEPDB1'
pytest tests/test_store_conformance.py -v

Oracle Cloud's Always Free ATP is enough.

Things that will bite you

Archive and staging twins are handled, but know how. If your warehouse has
orders, orders_bkp and stg_orders, the copies carry the same name words in
a shorter document, and cosine similarity likes short documents. Left alone,
a three-column _tmp copy beats the twenty-five-column table it was copied
from, even with a hint on the real one — I watched it happen. So an object
whose name is a real object's name plus _bkp, _old, _tmp, _v2,
_archive and so on, or stg_/tmp_ in front, is ranked below the object it
shadows. Only when that object exists: a lone pricing_v2 with no pricing
is left alone. Only in the same schema. And never when you name the copy
outright — asking for fact_claim_line_v2 gets you fact_claim_line_v2. The
lists are DEFAULT_SHADOW_SUFFIXES and DEFAULT_SHADOW_PREFIXES; pass your
own to Catalog(...), or empty tuples to switch it off. cat.shadows() shows
what was detected.

Identifier length. PostgreSQL truncates names to 63 bytes at creation.
That's the database doing it, not schemagate, and there's nothing to be done from
this side.

Non-English schemas work, including Chinese, Japanese and Korean, and
accents fold both ways so a search for facturacion finds facturación. But a
question in English will not find a table named in Spanish. Nothing lexical can
bridge that. Descriptions can.

top_k is not a hard cap. Foreign-key expansion runs after selection and
adds join tables on top. That's deliberate — SQL that references a table you
didn't include won't run — but size your prompt budget for it.

Status

v0.1. Alpha, and the API may still move.

Reflection	certified on SQLite, PostgreSQL 16, Oracle 26ai, MySQL 8.4 and SQL Server 2022
MemoryStore	done
AI cataloging	done, tested offline against fake providers
CLI	done
Studio (schemagate studio, and the hosted demo)	done, driven by a real browser in tests
MCP server	done, tested through a real MCP client
LangChain retriever	done, tested against langchain-core
OracleStore	written and statically verified, needs a live run
pgvector store	not started

import schemagate never imports any provider SDK, and there's a test asserting it.

Default embeddings are stable across processes, machines and Python versions,
so cached or persisted vectors stay valid. That one is enforced by a test that
runs the embedder in fresh subprocesses under different PYTHONHASHSEED
values, because it was broken once and nothing else caught it.

Apache-2.0. Ashish Sinha.