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SQL Server / Azure SQL for agents: real schema, lint, read-only runs, plans, write previews.
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SQL Server / Azure SQL for agents: real schema, lint, read-only runs, plans, write previews.
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What You'll Need
Set these up before or after installing:
Environment variable: SQLGLASS_CONFIG
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-s-curvelabs-sqlglass": {
"env": {
"SQLGLASS_CONFIG": "your-sqlglass-config-here"
},
"args": [
"sqlglass"
],
"command": "uvx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
sqlglass
An MCP server that gives GitHub Copilot (VS Code agent mode) — or any MCP client — what it lacks when writing SQL for SQL Server / Azure SQL: the real schema, a safe way to try a query, the optimizer's opinion of it, and a managed library of the queries you keep.
| Area | Tools |
|---|---|
| Schema (cached, works offline after first read) | list_connections refresh_schema list_tables describe_table search_schema find_join_path |
| Build & check (no database needed) | build_select lint_sql analyze_sql format_sql translate_sql |
| Execute, read-only | run_query explain_query sample_table profile_table |
| Writes, as a dry run | preview_write — an UPDATE / DELETE / INSERT becomes the SELECT that shows what it would do |
| New objects, as script text | build_create_table build_procedure build_view |
Query library (.sql files in git) | list_queries get_query save_query delete_query find_usage lint_library rename_in_library extract_parameter list_snapshots restore_snapshot |
Read-only, in layers
- Guard – the statement is tokenised; anything but
SELECT/WITH … SELECT(plusDECLARE/SET @var) is refused before a connection is opened. Because T-SQL needs no semicolons, any write/DDL/EXECkeyword anywhere outside a string, comment or[quoted name]rejects the batch — includingSELECT … INTO,OPENROWSET,sp_*/xp_*,WAITFOR. - Transaction – every query runs with autocommit off and is always rolled back. The ODBC connection is opened
read-only with
ApplicationIntent=ReadOnly. - Limits – rows are capped (
max_rows, default 200) and queries time out (timeout_seconds, default 30).explain_queryusesSET SHOWPLAN_XML ON: the server compiles the query and executes nothing. - Yours to add – point the connection at a login that only has
db_datareader(+GRANT SHOWPLANfor plans). That is the layer the server itself enforces; use it for anything that matters.
Values go in as bound parameters (params={"@Start": "2026-01-01"}), not pasted into SQL text.
Passwords are never stored: SQL-auth connections name an environment variable (password_env).
Writes and DDL: previewed and scripted, never executed
The server has no write mode. Instead:
preview_write turns one write statement into read-only SQL (plus a COUNT(*) of affected rows) and can run it:
UPDATE h SET h.Status = 'CLOSED' FROM dbo.PoHeader h JOIN dbo.Vendor v ON ... WHERE v.Country = 'US'
-- becomes
SELECT [h].[PoId], [h].[Status] AS [Status (current)], 'CLOSED' AS [Status (new)]
FROM dbo.PoHeader AS h JOIN dbo.Vendor AS v ON ... WHERE v.Country = 'US'
UPDATE → key columns + a current/new pair per SET column (changed_only=true hides no-op rows, NULL-safe via EXCEPT);
DELETE → the rows that would go, plus which child tables reference them; INSERT → the rows that would be added under
the target's column names, plus NOT NULL columns left unsupplied. MERGE is refused with advice to split it. The generated
text must itself pass the read-only guard before it is returned. Triggers, cascades and constraint failures are not simulated.
build_create_table / build_procedure / build_view return DDL text (sql, undo_sql, notes) for a person to
review and run in SSMS. Checked against the cached schema: the name must be free, foreign keys must reference a real
primary/unique key with matching types (and get an index), constraints get conventional names, CREATE TABLE is wrapped
in IF OBJECT_ID(...) IS NULL. build_procedure(query="open-po-value-by-vendor") wraps a library query and turns its
header params into typed procedure parameters; every @variable must be declared. save_query(kind="script") keeps a
script in the library — versioned with the queries, never linted as a query, never runnable through the server.
Install
pip install sqlglass # or run it without installing: uvx sqlglass
Needs Python 3.11+ and a SQL Server ODBC driver. The legacy SQL Server driver that ships with Windows works for
Windows/SQL authentication; Azure SQL / Entra ID sign-in needs "ODBC Driver 18 for SQL Server". On Linux/macOS,
install unixODBC plus Microsoft's ODBC driver.
Copy sqlglass.example.toml to sqlglass.toml in your workspace (or in
%LOCALAPPDATA%\sqlglass\) and define your connections. Passwords never go in the file.
VS Code / Copilot. Add the server to the user-level %APPDATA%\Code\User\mcp.json so it works from every
window (a workspace .vscode/mcp.json only loads once you trust that workspace's MCP servers):
"sqlglass": {
"type": "stdio",
"command": "uvx",
"args": ["sqlglass"],
"env": { "SQLGLASS_WORKSPACE": "C:\\path\\to\\your\\project", "REPORTING_SQL_PASSWORD": "${input:reporting-sql-pwd}" }
}
with a matching "inputs": [{ "id": "reporting-sql-pwd", "type": "promptString", "password": true, "description": "..." }].
VS Code asks for the password once and keeps it in its secret storage — never put the value in the file, and do not
rely on a user environment variable: a VS Code process started before the variable existed will never see it.
Verified 2026-09-21 with Copilot agent mode (GPT-5.6): list_tables → describe_table ×3 → build_select → lint_sql →
explain_query → run_query, 19 steps, results identical to a direct run.
Config lookup order: $SQLGLASS_CONFIG → $SQLGLASS_WORKSPACE\sqlglass.toml → .\sqlglass.toml → %LOCALAPPDATA%\sqlglass\sqlglass.toml.
Schema cache and snapshots live under %LOCALAPPDATA%\sqlglass\ (override with SQLGLASS_HOME).
The query library
A folder of plain .sql files; the id is the path without .sql. Each opens with a header:
-- name: Open POs by vendor
-- description: Open purchase-order value per vendor since a start date.
-- connection: erp
-- tags: purchasing, monthly
-- param: @StartDate date = '2026-01-01' | first order date to include
SELECT v.Name, SUM(l.Amount) AS OpenValue
FROM dbo.PoHeader AS h
JOIN dbo.Vendor AS v ON v.VendorId = h.VendorId
...
WHERE h.OrderDate >= @StartDate
The body does not declare its parameters — the server does that when running it (in SSMS, add the DECLAREs
yourself). Git is the history; on top of that every write through the server takes a snapshot first
(restore_snapshot undoes it), returns a diff, and supports dry_run.
find_usage("dbo.Vendor", "Name")– which saved queries break if this changes.rename_in_library– follow a table/column rename through every query, token-aware (strings/comments untouched).lint_libraryafterrefresh_schema– finds queries that reference tables/columns that no longer exist.
Lint rules
Correctness: join-without-on comma-join not-in-subquery left-join-filtered-in-where top-without-order-by
between-date-end undeclared-parameter unused-parameter parameter-declared-twice unused-cte
· Performance: non-sargable-predicate leading-wildcard-like select-star nolock distinct-over-join union-distinct
· Style: order-by-ordinal missing-schema-prefix unqualified-column
· With a cached schema: unknown-table unknown-column (with did-you-mean).
Layout
src/sqlglass/ — tsql/ (lexer, read-only guard, sqlglot analysis) → pure lint / refactor / builder / preview / ddl / plan
(SHOWPLAN XML summariser) / schema (model + cache + FK join paths) → engines/ (mssql over pyodbc, sqlite for
tests and local files) → library + snapshots → server.py (the tools).
Development
git clone https://github.com/S-CurveLabs/sqlglass; cd sqlglass
python -m venv .venv
.venv\Scripts\pip install -e .[dev]
.venv\Scripts\pytest
Everything except the live SQL Server path runs against a SQLite fixture. To exercise engines/mssql.py, define a
connection and run set SQLGLASS_TEST_CONNECTION=<name> then pytest -m mssql.
Not built yet
A write mode (by design), reading existing procedure / view definitions from the database, MERGE previews, actual (post-execution) plans and STATISTICS IO,
Postgres/MySQL engines, SQL embedded in Power Query (Value.NativeQuery).
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