amnesic 0.1.8

The MCP server with the most ironic name in the registry — persistent semantic memory for your SQL databases

amnesic — the MCP server with the most ironic name in the registry

Persistent semantic memory for your SQL databases. The name is ironic — it remembers everything.

"The MCP server with the most ironic name in the registry. It's anything but amnesic — it remembers your database so your AI doesn't have to."

🔒 Read-only by design. amnesic refuses to execute INSERT, UPDATE, DELETE, DROP, TRUNCATE, ALTER, CREATE, EXEC, MERGE, GRANT, REVOKE — and any write statement smuggled inside a WITH CTE. Two layers of defense: static SQL analysis rejects the statement before connecting, and every query runs inside a transaction that is immediately rolled back. Safe to point at prod. Details ↓

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The problem

Every session with an AI starts cold. You spend the first few minutes re-explaining what tables exist, what a status column value of 3 means, which FK connects orders to users. Then the session ends, and you do it all over again tomorrow.

amnesic fixes this. It gives your AI a persistent SQLite knowledge store — one per database — that survives across sessions. Annotate a status enum once; every future session sees those labels automatically. Discover FK relationships once; every future JOIN query uses that graph.

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Quickstart (90 seconds)

pipx install "amnesic[mssql]"   # swap in [postgres], [mysql], [sqlite], or [all]
amnesic init                    # interactive wizard

The wizard:

• Asks for your database type, host, and credentials
• Tests the connection before saving anything
• Stores the password securely in ~/.config/amnesic/.env (chmod 600)
• Writes the connection block to ~/.config/amnesic/connections.toml

Then add amnesic to your AI client and restart.

Don't have pipx? Or prefer uv / plain pip?

Install pipx (one-time):

brew install pipx                                  # macOS
sudo apt install pipx                              # Linux (Debian/Ubuntu)
python -m pip install --user pipx                  # Windows / generic

Or use uv (single-binary alternative — fast, no Python required):

brew install uv                                            # macOS
curl -LsSf https://astral.sh/uv/install.sh | sh            # Linux / macOS
powershell -c "irm https://astral.sh/uv/install.ps1 | iex" # Windows

uv tool install "amnesic[mssql]"

Or plain pip (installs into your active Python env):

pip install "amnesic[mssql]"

After install, amnesic --help works from any terminal.

Where amnesic stores things

File	macOS / Linux	Windows
Config	~/.config/amnesic/connections.toml	%APPDATA%\amnesic\connections.toml
Secrets	~/.config/amnesic/.env (chmod 600)	%APPDATA%\amnesic\.env (user profile ACL)
Knowledge	~/.config/amnesic/knowledge_<name>.db	%APPDATA%\amnesic\knowledge_<name>.db

Set $AMNESIC_HOME (or $XDG_CONFIG_HOME on Linux) to override the location.

Adding more connections later

amnesic add          # add another connection to existing config
amnesic test         # verify all connections
amnesic test orders.prod  # verify one connection

Setting and rotating passwords

amnesic init and amnesic add save your password automatically — for the typical setup flow, you never need to think about this section.

Use set-secret when you need to change a stored password later — IT rotated it, you mistyped it during setup, or you're hand-editing the config.

$ amnesic set-secret ORDERS_PROD_PASSWORD
Value: ****            ← hidden input (your typing is invisible)
Confirm: ****
✓ Set ORDERS_PROD_PASSWORD in ~/.config/amnesic/.env

What's the variable name? It's the env var your connections.toml references for that connection's password. The wizard auto-generates these as <CONNECTION_NAME_UPPERCASE_WITH_UNDERSCORES>_PASSWORD:

Connection name	Generated env var
orders.prod	ORDERS_PROD_PASSWORD
analytics	ANALYTICS_PASSWORD
drive.staging	DRIVE_STAGING_PASSWORD

To see the exact name your config uses, check ~/.config/amnesic/connections.toml — anything inside ${...} is the variable to pass to set-secret.

Under the hood: writes (or replaces) the line in ~/.config/amnesic/.env, sets file permission to chmod 600 (only your user can read it), preserves all other entries.

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Add to your AI client

Once amnesic is installed with the right driver extras (see Quickstart), the amnesic command is on your PATH. Use the same snippet across every MCP client:

Claude Code

Add to ~/.claude/mcp.json:

{
  "mcpServers": {
    "amnesic": {
      "command": "amnesic"
    }
  }
}

Claude Desktop

Add to your platform's Claude Desktop config:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "amnesic": {
      "command": "amnesic"
    }
  }
}

Cursor

Add to .cursor/mcp.json in your project (or ~/.cursor/mcp.json globally):

{
  "mcpServers": {
    "amnesic": {
      "command": "amnesic"
    }
  }
}

Without a global install (ephemeral)

If you'd rather not install amnesic on your system, use uvx or pipx to fetch it each time the MCP client starts. Note the driver extras must be passed explicitly:

// uvx — requires `uv` installed (see Install section for per-OS instructions)
{
  "mcpServers": {
    "amnesic": {
      "command": "uvx",
      "args": ["--from", "amnesic[mssql]", "amnesic"]
    }
  }
}

// pipx — usually pre-installed via Homebrew or system package manager
{
  "mcpServers": {
    "amnesic": {
      "command": "pipx",
      "args": ["run", "--spec", "amnesic[mssql]", "amnesic"]
    }
  }
}

Swap mssql for postgres, mysql, or all to match the driver(s) you need.

VS Code (with MCP extension)

Add to .vscode/mcp.json:

{
  "servers": {
    "amnesic": {
      "type": "stdio",
      "command": "amnesic"
    }
  }
}

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Tools

Tool	Description
db_list_connections()	List all configured connections (no secrets exposed)
db_list_tables(connection)	All known tables with descriptions and column counts
db_search(query, connection, target, limit)	BM25 search over table/column descriptions and aliases
db_get_schema(table, connection)	Column schema merged with saved annotations
db_query(sql, connection)	Execute a read-only SELECT query
db_annotate(table, connection, ...)	Persist semantic annotations for tables/columns
db_sync_knowledge(from, to)	Copy annotations between connections (e.g. staging → prod)
db_discover_relationships(connection)	Discover all FK relationships from the live DB
db_get_relationships(table, connection)	Navigate the FK graph for JOIN planning

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Searching the knowledge base

For large schemas, db_list_tables is impractical — you'd dump 500+ rows into Claude's context. Use db_search to find the relevant tables/columns by keyword instead:

"What table tracks customer payments?"
  → db_search("payments")
    Top results:
      - dbo.payments  (table) "Customer payment records..."
      - dbo.orders.payment_method  (column) "Mode of payment..."

db_search uses SQLite FTS5 with BM25 ranking — fast, local, no embeddings or external services. Search syntax supports:

Syntax	Effect
payment	Match the word (with stemming — also matches "payments", "paying")
"payment method"	Exact phrase
pay*	Prefix match — "payment", "payable", etc.
payment AND status	Both terms required
payment OR refund	Either term

Results return ranked table/column rows with descriptions and highlighted snippets.

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The knowledge layer

The core differentiator. Every annotation survives restarts, model updates, and new sessions.

Session 1 — you discover something

You: What does status=3 mean in the orders table?
AI: Let me check. [runs db_query: SELECT DISTINCT status FROM dbo.orders]
    I see values 1, 2, 3, 4. Let me look at some examples...
    Based on the data, 3 appears to be "cancelled".

You: Save that. And status=1 is "pending", 2 is "confirmed", 4 is "delivered".
AI: [calls db_annotate]
    db_annotate(
      table="dbo.orders",
      column="status",
      column_description="Order lifecycle status",
      enum_values={"1": "pending", "2": "confirmed", "3": "cancelled", "4": "delivered"}
    )
    Saved. Future sessions will see these labels automatically.

Session 2 — the knowledge is already there

You: How many cancelled orders are there this month?
AI: [calls db_get_schema("dbo.orders")]
    Schema response includes:
      column: "status"
      description: "Order lifecycle status"
      enum_values: {"1": "pending", "2": "confirmed", "3": "cancelled", "4": "delivered"}

    [writes correct SQL immediately]
    SELECT COUNT(*) FROM dbo.orders WHERE status = 3 AND ...

No re-discovery. No wasted turns. The annotation persisted.

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Relationship graph

Understand your schema's JOIN structure once, reuse it forever.

AI: [db_discover_relationships(connection="orders.prod")]
    Discovered 47 foreign key relationships.

AI: [db_get_relationships(table="orders", depth=2)]
    neighbors:
      orders → users (via user_id → id)
      orders → order_items (via id ← order_id)
    paths:
      orders -> users
      orders -> order_items
      order_items -> products

Now the AI knows exactly how to JOIN across your schema without guessing.

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Sync between environments

Build up annotations in staging, then promote to prod:

db_sync_knowledge(from_connection="orders.staging", to_connection="orders.prod")

Returns {synced: [...], skipped: [{table, reason}], warnings: [{table, column, reason}]}.

Tables missing from the target schema cache are skipped with a clear reason. Columns missing from target schema are warned but don't block the rest of the sync.

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Advanced: hand-edit the TOML

If you prefer to manage the config file yourself, generate a blank template:

amnesic init --template

This writes ~/.config/amnesic/connections.toml with commented examples and exits — no wizard. Edit the file directly:

# ~/.config/amnesic/connections.toml

# Nested style: [connections.product.env]
[connections.orders.prod]
driver = "mssql"
server = "localhost"
port = 11433
database = "OrdersDB"
user = "${ORDERS_USER}"
password = "${ORDERS_PROD_PASSWORD}"
tunnel_script = "~/.scripts/mssql-tunnel.sh"     # macOS / Linux (bash)
# tunnel_script = "C:/scripts/mssql-tunnel.ps1"  # Windows (PowerShell)

[connections.orders.staging]
driver = "mssql"
server = "localhost"
port = 11434
database = "OrdersDB_Staging"
user = "${ORDERS_USER}"
password = "${ORDERS_STAGING_PASSWORD}"

# Flat style: [connections.name]
[connections.analytics]
driver = "postgres"
server = "analytics.company.com"
port = 5432
database = "warehouse"
user = "${ANALYTICS_DB_USER}"
password = "${ANALYTICS_DB_PASSWORD}"

# SQLite — no credentials needed
[connections.local]
driver = "sqlite"
database = "/absolute/path/to/local.db"       # macOS / Linux
# database = "C:/path/to/local.db"            # Windows (use forward slashes)

Use ${ENV_VAR} for credentials — never hardcode passwords.

Secrets are loaded from ~/.config/amnesic/.env automatically (format: KEY=VALUE, one per line, # for comments). For each ${VAR_NAME} referenced in your TOML, populate the matching .env entry with amnesic set-secret VAR_NAME (hidden input, chmod 600), or write .env yourself.

Canonical connection names use dot notation: orders.prod, orders.staging, analytics, local.

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Supported databases

Database	Driver	Extra
PostgreSQL	psycopg2	pip install "amnesic[postgres]"
MySQL / MariaDB	pymysql	pip install "amnesic[mysql]"
Microsoft SQL Server	pymssql	pip install "amnesic[mssql]"
SQLite	built-in	no extra needed

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Safety & read-only enforcement

amnesic is built to be safe to point at production databases.

Why your AI can't damage your data

Every query passes through two independent layers before reaching the database:

1. Static analysis (in amnesic/readonly.py) — the SQL is tokenized and rejected if it contains any of: INSERT, UPDATE, DELETE, DROP, TRUNCATE, ALTER, CREATE, EXEC, EXECUTE, MERGE, BULK, GRANT, REVOKE, DENY. This includes write statements smuggled inside CTEs (WITH x AS (SELECT ...) UPDATE ... is caught and refused).
2. Transaction rollback — even if a write statement somehow gets past the static check, the query runs inside BEGIN TRANSACTION ... ROLLBACK so nothing is ever committed. Belt and suspenders.

Only SELECT and WITH ... SELECT reach the database. Comments are stripped before analysis so /* DELETE FROM users */ can't be used to hide an attack.

Other safety measures

• No credentials in responses: db_list_connections strips passwords and usernames from its output. The AI can see which connections exist, never how to authenticate to them.
• Credentials via env vars only: ${ENV_VAR} expansion at config-load time — passwords never touch connections.toml on disk.
• Secure .env storage: on macOS/Linux chmod 0o600 (owner read/write only); on Windows the .env lives in %APPDATA% which is restricted to your user profile by Windows ACL.
• Identifier validation: table/schema/database names are checked against [A-Za-z0-9_]+ before any string interpolation into SQL.
• Tested: 40+ unit tests in tests/test_readonly.py cover every write keyword, comment-stripping edge case, CTE-with-write attempts, semicolon-separated multi-statements, and identifier injection attempts. pytest tests/test_readonly.py to verify on your machine.

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Roadmap

What's coming: knowledge lifecycle management (v0.2 — db_deprecate, drift detection, export/import for team handoff), query intelligence (v0.3 — db_explain, query history), team sharing (v0.4), and more. See ROADMAP.md for the full picture.

Have an idea? Open an issue.

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Track usage

pypistats.org/packages/amnesic

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License

MIT — see LICENSE.

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MCP Registry

This server is registered on the official MCP Registry.

mcp-name: io.github.SurajKGoyal/amnesic