Anvaya Labs — Universal Database MCP (Simple Version)

A small MCP server that lets an LLM query PostgreSQL, MySQL, MariaDB, SQL Server, or SQLite databases safely — read-only, role-restricted, and with sensitive data blacked out.

Files (4 files, ~650 lines total, no classes/decorators/async)

• config.py — plain dictionaries: who can see what, which columns get masked. Edit this file to change any rule.

• security.py — 4 plain functions: check it's read-only, check the role is allowed, add tenant filtering, mask sensitive values.

• db_engine.py — 3 plain functions: get schema, run query, explain query. Talks to the actual database.

• server.py — the 4 tools the LLM can call, each just a try/except around the functions above, with a log line either way.

Related MCP server: mcp-multi-db

Keeping database passwords out of chat

db_uri no longer has to be a raw connection string typed into the conversation. Instead:

1. Copy .env.example to .env and fill in real connection strings there.

2. In chat, refer to a database by its short name — e.g. "query the prod database" — and pass db_uri="prod". The server looks up ANVAYA_DB_PROD in the environment and uses that.

3. Leaving db_uri empty ("") uses ANVAYA_DEFAULT_DB_URI instead.

A full connection string (containing ://) still works directly if you pass one — useful for quick local testing — but the recommended pattern is to never type a real password into the chat at all.

Authentication (for remote/multi-team deployments)

Since other people connect to this server, role can no longer be trusted as something the LLM just tells the server — anyone could type role="admin". Instead:

1. Generate a secret once and put it in .env:

   python -c "import secrets; print(secrets.token_hex(32))"

   ANVAYA_JWT_SECRET=<paste it here>
   ANVAYA_MCP_TRANSPORT=http

2. Whenever someone needs access, mint them a signed token:

   uv run python issue_token.py --name alice --role analyst --tenant acme

3. Give that token to them. Their MCP client connects with it as a Bearer token. The server verifies the signature on every call and uses the role/tenant from the token — any role/tenant_id they try to pass as arguments is ignored.

If ANVAYA_JWT_SECRET isn't set, the server runs with no auth at all (fine for local stdio testing on your own machine, not for anything reachable by other people).

Transport: this uses Streamable HTTP (transport="http"), the current MCP standard for remote servers — SSE is deprecated as of the 2025-11-25 MCP spec revision.

Deploying for free (Render) so claude.ai (web) can reach it

claude.ai connects to remote MCP servers from Anthropic's cloud, not from your browser — so this needs a real public HTTPS URL. Local network / VPN-only hosting won't work for the web client (Claude Desktop's local stdio config is different and stays working as-is).

Render gives you that for free, with auto-deploy on every git push.

1. Push this project to a GitHub repo

git init
git add .
git commit -m "Anvaya Universal Database MCP"
git remote add origin https://github.com/YOUR_USERNAME/anvaya-labs-mcp.git
git push -u origin main

2. Create the Render service

1. Go to render.com → sign up (no credit card needed for the free tier) → New → Web Service

2. Connect your GitHub repo

3. Settings:

   • Runtime: Python 3

   • Build Command: pip install -r requirements.txt

   • Start Command: python server.py

   • Instance Type: Free

3. Add environment variables (Render dashboard → Environment)

ANVAYA_JWT_SECRET=<your generated secret>
ANVAYA_MCP_TRANSPORT=http
ANVAYA_DEFAULT_DB_URI=sqlite:///test.db

(test.db is committed in this repo as a small demo database — Render's free tier has no persistent disk, so a database that's part of the codebase is what survives redeploys. For real data, point ANVAYA_DEFAULT_DB_URI at an externally hosted database instead — e.g. a free Postgres from Neon — rather than SQLite.)

4. Deploy

Render builds and deploys automatically. You'll get a URL like https://anvaya-labs-mcp.onrender.com. Every future git push to this repo redeploys automatically — no extra steps needed.

Two honest limitations of the free tier:

• The service sleeps after 15 minutes of no traffic, and the first request after that takes 30-50 seconds to wake up — the very first tool call after idling may feel slow or briefly time out.

• No persistent disk, as noted above — anything written to disk at runtime disappears on the next restart or deploy.

5. Issue a token and add the connector in claude.ai

uv run python issue_token.py --name alice --role analyst --tenant acme

Then in claude.ai: Settings → Connectors → Add custom connector

• URL: https://anvaya-labs-mcp.onrender.com/mcp

• Open Request headers (this is a beta feature — if you don't see it, it may not be rolled out to your account yet)

• Header name: authorization

• Header value: Bearer <the token you issued>

Testing with realistic data across all roles

generate_test_data.py builds a bigger test.db — 4 tables, 30 rows each, spread across 3 tenants (acme, globex, initech) — designed to exercise every rule in ROLE_PERMISSIONS:

uv run python generate_test_data.py

Then run the full role test battery:

uv run python test_all_roles.py

This checks things like: admin can reach every table but still can't write; analyst gets auto-scoped to one tenant and can't see password_hash; support can't see payments at all; readonly_guest can only ever see products. One thing worth knowing: masking is unconditional — even admin sees email/card_number redacted, since masking isn't role-aware in this version, only RBAC table/column access is.

If you change test.db, remember to git push so Render picks up the new version on its next deploy (it has no persistent disk, so the committed file is what it actually serves).

Before shipping to a client

Two things worth doing every time, before a client's database is connected for real:

1. Error messages no longer leak internals. Unexpected errors (a bad connection string, a driver failure, anything not deliberately raised by our own RBAC/validation code) now return only a generic message plus a short reference code — the real detail goes only to the server-side log (stderr), tagged with that same code. Our own deliberate messages (like "Role 'analyst' is not allowed to access column 'x'") are unaffected and still show clearly, since those are safe by design.

2. Check your RBAC config against the real database before go-live:

uv run python validate_config.py <db_uri or connection name>

This catches table/column name mismatches between config.py's ROLE_PERMISSIONS and whatever database you actually point it at — e.g. a table you renamed but forgot to update in the config, or a table nobody remembered to explicitly allow or deny for a given role. It exits non-zero if it finds a real mismatch, so it's safe to wire into a pre-deploy check if you want.

OAuth mode (for claude.ai web, which only supports OAuth connectors)

Bearer tokens (above) work great for Claude Desktop and Claude Code, but claude.ai in a browser currently only offers OAuth as a connector auth option (a "Request headers" beta exists but isn't available to every account yet). For that, this project includes a full, self-hosted OAuth 2.1 authorization server — a real login page, not just a token check.

Read this before using it in production: building your own OAuth server is something FastMCP's own documentation says most people shouldn't do — it's included here because claude.ai web specifically requires it and no external identity provider was wanted. The security-critical cryptography (PKCE verification, redirect URI validation) is handled by the underlying MCP SDK, not custom code here — but you're still responsible for everything else (the login page, code/token bookkeeping, who's allowed to log in). See the limitations listed at the top of oauth_provider.py.

1. Turn it on

ANVAYA_JWT_SECRET=<your secret>
ANVAYA_AUTH_MODE=oauth
ANVAYA_MCP_TRANSPORT=http
ANVAYA_PUBLIC_BASE_URL=https://your-real-public-url.onrender.com

2. Add people who are allowed to log in

uv run python manage_oauth_users.py add --username alice --role analyst --tenant acme
uv run python manage_oauth_users.py list
uv run python manage_oauth_users.py remove --username alice

You'll be prompted for a password (not shown in your terminal history). This creates oauth_users.json locally — never commit this file (it's already in .gitignore).

3. Add the connector in claude.ai

Settings → Connectors → Add custom connector — just the URL:

https://your-real-public-url.onrender.com/mcp

claude.ai discovers everything else automatically (it registers itself as an OAuth client, then redirects you to your server's login page). When you connect, you'll see the login form built into this project — sign in with a username/password from step 2.

What you get, and what you don't

• ✅ Real login, on claude.ai web, with role/tenant decided by who logged in — not a header anyone could paste in.

• ✅ PKCE, redirect URI validation, and authorization-code replay protection all verified working (see the test files this was built and checked against).

• ❌ No refresh tokens — when the 24-hour access token expires, the person just logs in again.

• ❌ Registered OAuth clients and issued codes are in-memory — a server restart clears them (claude.ai will just re-register/re-login automatically next time it connects).

Setup

uv sync
uv run anvaya-mcp

The 4 tools

• get_database_schema(db_uri, role)

• execute_safe_query(db_uri, sql_query, role, tenant_id)

• explain_sql_query(db_uri, sql_query)

• export_results_format(db_uri, sql_query, role, format_type, tenant_id)

Roles

Defined in config.py under ROLE_PERMISSIONS: admin, analyst, support, readonly_guest. Edit that dictionary to add roles, change which tables/columns they can see, or turn tenant filtering on/off.

What got simplified from the first version

• Sync database calls instead of async (easier to read top-to-bottom)

• No decorators, dataclasses, or Enums — just dicts and functions

• Row-level tenant filtering only supports a single tenant_id column (not multiple candidate column names)

• Masking is column-name-based only (no scanning cell contents for patterns like card numbers)

Connections and schema are cached (see db_engine.py — ENGINE_CACHE and SCHEMA_CACHE, two plain dictionaries), so repeated calls reuse the same connection instead of opening a new one every time, and don't re-fetch the schema on every call. The schema cache refreshes itself every 5 minutes, or immediately if you pass force_refresh=True.

Everything from your original feature list is still here — schema discovery, relationship mapping, read-only enforcement, EXPLAIN, table/column RBAC, row-level filtering, data masking, CSV/Excel/JSON export, and audit logging — just written as plainly as possible.