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>

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.