Amnesic

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

[](https://pypi.org/project/amnesic/) [](https://pypi.org/project/amnesic/) [](LICENSE) [](https://registry.modelcontextprotocol.io) [](https://glama.ai/mcp/servers/SurajKGoyal/amnesic)

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."

Works with Claude Code · Claude Desktop · Cursor · VS Code · Cline · Windsurf — any MCP-compatible client.

Available on Official MCP Registry · Claude Code plugin marketplace

> 🔒 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 ↓](#safety--read-only-enforcement)

---

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            # install the core
amnesic init                    # interactive wizard

> ⚡ Try it without credentials. Run amnesic init --demo instead — it adds a self-contained SQLite sample DB (e-commerce schema: customers / products / orders with FKs and an enum column) so you can exercise every tool in under a minute. Great for a first look before pointing amnesic at a real database.

The wizard asks which database type you're connecting to and tells you the one command to run if its driver isn't installed yet — you never need to guess extras up front.

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](#add-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

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

pip install amnesic

> Whichever you pick, amnesic init asks which database you'll connect to and prints the one extra command to install that driver — no need to commit to extras up front.

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_.db | %APPDATA%\amnesic\knowledge_.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 _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.

Managing connections and knowledge

Knowledge accumulates per connection in a local SQLite file. These commands let you move it between machines and clean up:

# Hand off everything you've taught amnesic about a database (annotations +
# relationships, not the re-derivable schema cache) as portable JSON:
amnesic export orders.prod -o orders-knowledge.json
amnesic export orders.prod            # or print to stdout to pipe/redirect

# Load that knowledge into another connection (e.g. promote staging → prod,
# or onboard a teammate). Unconditional upsert — existing entries are overwritten:
amnesic import orders.prod orders-knowledge.json

# Wipe stored knowledge for a connection but keep the config entry:
amnesic clear orders.staging

# Drop a connection from connections.toml entirely (knowledge file kept
# unless you pass --delete-knowledge):
amnesic remove old.connection
amnesic remove old.connection --delete-knowledge

export/import/clear/remove operate purely on local files — they never connect to the database, so they work even if a connection's credentials aren't set. remove edits connections.toml with surgical string edits, leaving every other block's formatting and comments byte-for-byte intact.

---

Add to your AI client

Once amnesic is installed with the right driver extras (see [Quickstart](#quickstart-90-seconds)), the amnesic command is on your PATH. Use the same snippet across every MCP client:

Claude Code

One-line install (recommended — no JSON editing). Inside Claude Code:

/plugin marketplace add SurajKGoyal/amnesic-marketplace
/plugin install amnesic@amnesic

That wires amnesic as an MCP server automatically. Source: SurajKGoyal/amnesic-marketplace.

Or wire it by hand — edit ~/.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

One-click install — click the button below and Cursor wires it up for you:

Or wire it by hand — edit .cursor/mcp.json

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"]
    }
  }
}

For multiple drivers, comma-separate inside the brackets — e.g. amnesic[postgres,mssql] or use amnesic[all] for everything.

VS Code (with MCP extension)

Add to .vscode/mcp.json:

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

---

Updating

amnesic ships often. Upgrade with the same tool you installed it with:

| Installed via | Upgrade command | |---|---| | pipx | pipx upgrade amnesic | | uv tool | uv tool upgrade amnesic | | pip | pip install --upgrade amnesic | | uvx (ephemeral, in your MCP config) | uvx caches builds — run uv cache clean amnesic to pull the newest |

Then restart your MCP client (Claude Code, Cursor, …) so it relaunches the amnesic server and picks up any new tools.

Upgrading is safe — you won't lose annotations. Your knowledge files auto-migrate to the new schema on first load; amnesic only ever adds columns, never drops your data.

To check the installed version: amnesic --version. Latest release: PyPI · Releases.

---

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_deprecate(table, connection, column?, reason?, undo?) | Soft-retire a stale annotation — flagged (and warned) but kept, reversible | | db_detect_drift(connection) | Audit annotations vs the live schema — find orphaned annotations + undocumented tables | | db_forget(table, connection, column?, cascade?) | Hard-delete an annotation (cascade opt-in) — permanent | | 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 |

---

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.

---

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.

---

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.

---

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.

---

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](#setting-and-rotating-passwords) (hidden input, chmod 600), o

…

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: SurajKGoyal
• Source: SurajKGoyal/amnesic
• License: MIT

Install and usage instructions live in the source repository linked above.