amnesic 0.1.0

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."

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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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Install

# Core only (SQLite works out of the box)
pip install amnesic

# With driver extras
pip install "amnesic[postgres]"
pip install "amnesic[mssql]"
pip install "amnesic[mysql]"
pip install "amnesic[all]"

# Or run directly with uvx (no install needed)
uvx amnesic

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Setup

1. Create config

amnesic init

This creates ~/.config/amnesic/connections.toml with a commented template.

2. Edit the config

# ~/.config/amnesic/connections.toml

# Nested style: [connections.product.env]
[connections.orders.prod]
driver = "mssql"
server = "localhost"
port = 11433
database = "OrdersDB"
user = "${ORDERS_PROD_USER}"
password = "${ORDERS_PROD_PASSWORD}"
tunnel_script = "~/.scripts/mssql-tunnel.sh"  # optional SSH tunnel

[connections.orders.staging]
driver = "mssql"
server = "localhost"
port = 11434
database = "OrdersDB_Staging"
user = "${ORDERS_STAGING_USER}"
password = "${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 = "/Users/me/data/local.db"

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

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

3. Test connectivity

amnesic test              # tests all connections
amnesic test orders.prod  # tests one connection

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Add to your AI client

Claude Code

Add to ~/.claude/mcp.json:

{
  "mcpServers": {
    "amnesic": {
      "command": "uvx",
      "args": ["amnesic"]
    }
  }
}

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "amnesic": {
      "command": "uvx",
      "args": ["amnesic"]
    }
  }
}

Cursor

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

{
  "mcpServers": {
    "amnesic": {
      "command": "uvx",
      "args": ["amnesic"]
    }
  }
}

VS Code (with MCP extension)

Add to .vscode/mcp.json:

{
  "servers": {
    "amnesic": {
      "type": "stdio",
      "command": "uvx",
      "args": ["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_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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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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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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Security

• Read-only enforcement: two layers — static SQL analysis rejects any write/DDL statement before a connection opens, plus every query runs inside an immediately-rolled-back transaction.
• No credentials in responses: db_list_connections strips passwords and usernames from output.
• Credentials via env vars: ${ENV_VAR} expansion at load time — secrets never touch the config file on disk.
• Identifier validation: table names, schema names, and database names are validated against [A-Za-z0-9_] before any interpolation into SQL.

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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.