Supabase
Supabase’s official server lets AI create tables, query data and review project settings in your Supabase database.
Description from the maker (original English)
Read-only MCP for the SQL database behind an ERP: provably read-only guard, public benchmark.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
You need
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"erp-report-engine": {
"command": "uvx",
"args": [
"erp-report-engine",
"mcp",
"-c",
"your-value"
]
}
}
}Run this in your terminal
claude mcp add erp-report-engine -- uvx erp-report-engine mcp -c your-valueThis one needs values on its command line, so copy the settings and fill them in.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"erp-report-engine": {
"command": "uvx",
"args": [
"erp-report-engine",
"mcp",
"-c",
"your-value"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one needs values on its command line, so copy the settings and fill them in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"erp-report-engine": {
"type": "stdio",
"command": "uvx",
"args": [
"erp-report-engine",
"mcp",
"-c",
"your-value"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026
The registry name was verified with the GitHub account gulmezeren2-byte, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.7.0) reached the registry 4 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
In the same category: Supabase, MongoDB MCP Server, Elasticsearch MCP Server
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Supabase’s official server lets AI create tables, query data and review project settings in your Supabase database.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
The numbers for Monday's meeting are already sitting in your ERP's database. So who's still writing the report?
**A read-only, self-verifying data layer for the SQL database behind your ERP — weekly reports *and* a guarded MCP server for AI agents. Every query provably read-only: measured against 28 attacks, not promised in prose.**
🇹🇷 Türkçesi: README.tr.md
One scheduled run executes 9 audited SELECT statements and delivers a self-contained HTML report: four KPIs against an 8-week baseline, findings with named drivers, a data-quality gate, and row counts reconciled against the source. No BI license, no agent installed on the ERP server, and no writes — enforced in four layers (lexical, parse-tree, a side-effecting-function guard, and a read-only session), not promised in prose.
*This exact report was produced by one command against the bundled demo database — including the three data-quality problems deliberately seeded into it, all caught by the gate.*
The same guarded engine talks to AI agents, too. erp-report-engine mcp exposes the ERP to an agent through *canonical entities* (orders, never LG_001_01_ORFICHE) behind the same read-only guard — the layer the MCP ecosystem keeps getting wrong. The reference PostgreSQL MCP server's read-only mode was walked out of with COMMIT; DROP SCHEMA public CASCADE; and archived; Supabase's MCP became the textbook "lethal trifecta". Here the guard checks the functions a statement calls, not just its shape — and you don't have to take the adjective's word for it:
▶ [Run the 28-attack trust benchmark](https://gulmezeren2-byte.github.io/erp-report-engine/trust.html) · [break the guard yourself, in your browser](https://gulmezeren2-byte.github.io/erp-report-engine/playground.html) (the real guard.py, via Pyodide, nothing sent anywhere).
*The exact guard.py the tests run, executing in the browser — a file read and the read-only-transaction escape refused, a real aggregate allowed. Paste your own attack →*
# install (pipx or uv keep it isolated; plain pip works too)
pipx install erp-report-engine # or: uv tool install erp-report-engine
erp-report-engine init-demo # builds demo.db + config.demo.yaml here erp-report-engine run -c config.demo.yaml
Every command is also available as python -m erp_report_engine …. Add a database driver with the extras: pipx install "erp-report-engine[mssql]" (Logo Tiger / Netsis / Mikro — SQL Server) or [postgres].
Prefer Docker? docker build -t erp-report-engine . then docker run --rm -v "$PWD:/work" erp-report-engine run -c config.demo.yaml. SQLite and PostgreSQL work out of the box; for MSSQL add Microsoft's ODBC driver (see the Dockerfile). Publishing to PyPI is a GitHub Release away — publish.yml builds and uploads via Trusted Publishing (OIDC, no stored token), and CI already builds the wheel and asserts every bundled profile ships.
Open reports/erp_report_ .html. You'll see the engine catch a revenue spike and attribute it to one region, flag a two-point on-time decline, list items below two weeks of stock cover — and confess every duplicate and negative row it found on the way.
▶ See a live sample report: [gulmezeren2-byte.github.io/erp-report-engine](https://gulmezeren2-byte.github.io/erp-report-engine/) (also committed at docs/sample-report.html).
Or run run --dashboard for the premium Command Center — a dark, modern, self-contained dashboard with animated KPIs and glowing SPC control-band charts ([live](https://gulmezeren2-byte.github.io/erp-report-engine/dashboard.html)):
| Section | What it answers |
|---|---|
| KPI cards | Revenue, orders, on-time %, low-stock count — each vs last week and vs an 8-week baseline |
| Findings | *"Revenue +6.5% week-over-week — main driver: region 'Ege' (54% of the week's movement, partly offset by Marmara (−14,697))"* — driver named, the segment pulling the other way named too, action suggested |
| Signals (SPC) | *"Revenue signal: 148,291 is ABOVE the control limits (UCL 143,078 = mean 93,168 ± 2.66 × avg moving range 18,763, baseline n=25 weeks)"* — a genuine shift, separated from week-to-week noise, with the arithmetic and its sample size shown |
| Trends | 13 full weeks of revenue and on-time %, inline SVG (no external assets) |
| Stock attention list | Items below the cover threshold, worst first |
| Data-quality gate | Duplicate IDs, unparseable dates, negative totals, ship-before-order rows |
| Source reconciliation | Rows fetched vs an independent COUNT(*) of the same query — ✓ or MISMATCH |
| SQL audit trail | Every statement executed, with parameters, row counts and timings |
| Run-state memory | *"Revenue has declined 3 consecutive weeks"* — context beyond the lookback window |
flowchart LR
DB[("ERP database MSSQL · PostgreSQL · SQLite")] PROF["Semantic profile (YAML contract)"] GUARD["safe_read read-only guard + audit"] GATE["Data-quality gate
ISO weeks, 8-wk baseline"] INS["Insight rules driver attribution"] HTML["Self-contained HTML report"] STATE[("state.db run memory")]
PROF --> GUARD DB --> GUARD --> GATE --> KPI --> INS --> HTML STATE --- HTML GATE --> PBI["Power BI Command Center (TMDL + PBIR, code-authored)"]
The layer that makes this portable is the semantic profile: a versioned YAML contract that maps one ERP's cryptic schema to three canonical entities — orders, order_lines, inventory — plus an optional `receivables` entity (open AR, for aging) that a profile maps when the ledger is reachable and everything downstream skips gracefully when it isn't. The engine only ever sees canonical columns. Swap the profile, keep the report.
Pointing software at a production ERP database is a trust decision. This engine treats it that way — the guarantees are enforced in code and covered by tests:
Read-only is enforced in four layers, so no single mistake makes the engine capable of writing:
| Layer | Enforced by |
|---|---|
| Lexical guard | Single statement, SELECT/WITH head, no comments (--, /*, #), no write/DDL keyword, no write-escalating lock hint (TABLOCKX, UPDLOCK, XLOCK). Scanned with string literals blanked, so SELECT 'please delete this note' is a read, not a threat |
| Parse-tree guard | sqlglot parses the statement — and must succeed, or the query is refused: a guard that can't read a query can't vouch for it. It must be a single read query whose AST holds no INSERT/UPDATE/DELETE/CREATE/DROP/ALTER/MERGE/EXEC/INTO node (catches writes hidden inside CTEs) |
| Function guard | Plenty of pure-looking `SELECT`s are not reads. pg_read_file, lo_export (which *writes* a file), dblink (which dials out), OPENROWSET, LOAD_FILE, load_extension (arbitrary code), query_to_xml (arbitrary SQL), set_config (switches off the session backstop), SLEEP/BENCHMARK (denial of service) — all refused, by AST and lexically, because OPENROWSET is precisely what sqlglot cannot parse |
| Read-only session | PostgreSQL default_transaction_read_only=on, SQLite PRAGMA query_only, MySQL SET SESSION TRANSACTION READ ONLY + max_execution_time, and a per-statement timeout everywhere |
Ad-hoc SQL — the agent path — is stricter still. query and guarded_query run in strict mode, which default-denies *every function the guard cannot name*: sqlglot's function registry is the allowlist, since it knows the portable analytic functions and nothing that reads a file or opens a socket. All four bundled profiles pass it — they call no unrecognised function at all.
And the layer that isn't ours. MSSQL has no session-level read-only switch, so run the engine under a least-privilege, read-only login (db_datareader on MSSQL, a SELECT-only grant on PostgreSQL — ideally a read replica). The guard is defence in depth; the grant is the layer that holds if the guard has a hole. It has had one before: these function bypasses were found by auditing this repo, and are now pinned in tests/test_guard.py by name, per dialect.
Don't take my word for it — measure it. The guard ships with a reproducible trust benchmark: 28 well-formed-SQL attacks across four dialects that a shape-only guard waves through, every one refused, plus the legitimate reads that must still pass. The number is computed from a live guard run, not asserted in prose, and CI enforces it on every commit.
And "28/28" only means something next to what a weaker guard scores on the *same* 28. So the benchmark runs the corpus through the shortcuts real tools actually ship — a starts-with-SELECT head check and a write-keyword blocklist — and shows the gap: they refuse 6 and 9 of the 28, and the keyword scan even blocks a legitimate read whose text contains the word "delete", while this guard refuses all 28 and blocks none. A test pins that it strictly beats every such shortcut.
erp-report-engine trust-benchmark
▶ See the results: [the trust benchmark](https://gulmezeren2-byte.github.io/erp-report-engine/trust.html) — every case, its severity, and what it actually does. Or [break it yourself](https://gulmezeren2-byte.github.io/erp-report-engine/playground.html): paste SQL into the real guard, running in your browser (no install, nothing sent anywhere — it's the exact guard.py the tests run, via Pyodide).
Want just the guard for your *own* MCP server or DB
Only the beginning is shown; read the rest in the repo