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  1. Home
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  3. Amnesic
Amnesic logo
Health: ActiveRecent health check succeeded.Last checked 9/6/2026, 10:47:48 PM

Amnesic

User RatingsBe the first to rate and review this MCP server!
View Repository3 GitHub StarsTotal stargazers on GitHub for the source repository (3 stars).Visit Website
databasessqlschemamemoryread-only

Persists SQL schema annotations, foreign-key relationships, and searchable notes across AI sessions with read-only database access.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag β€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "surajkgoyal-amnesic": {
      "command": "uvx",
      "args": [
        "caches"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing AlternativesπŸ—„οΈ More in Databases

Overview

Amnesic gives each configured database a persistent SQLite knowledge store for table and column annotations, FK relationships, and searchable notes. It supports PostgreSQL, MySQL, MSSQL, and SQLite, and is intended for agents that need reusable schema context across sessions. Database access is read-only by design.

Use cases

β€’Annotate status codes and enum values once for reuse in later sessions
β€’Record and query foreign-key relationships when generating JOINs
β€’Search persistent notes about tables and columns
β€’Inspect production databases without allowing write statements

Key features

β€’Persistent per-database SQLite knowledge stores
β€’Table and column annotations
β€’Foreign-key relationship graph
β€’Searchable database notes
β€’PostgreSQL, MySQL, MSSQL, and SQLite support
β€’Static SQL checks and transaction rollback for read-only enforcement

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Amnesic.

Extracted Tool Capabilities
Persistent per-database SQLite knowledge stores
Table and column annotations
Foreign-key relationship graph
Searchable database notes
PostgreSQL, MySQL, MSSQL, and SQLite support
Static SQL checks and transaction rollback for read-only enforcement

Documentation Overview

amnesic β€” the MCP server with the most ironic name in the registry

PyPI version Python License: MIT MCP Registry Glama score

Your database's institutional memory, as an MCP server. The name is ironic β€” it remembers everything.

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

Most database MCP servers are query executors: they connect, introspect, run SQL, and forget. amnesic is a semantic memory β€” it accumulates what your schema means (what status = 3 is, which columns are really foreign keys, what that legacy table is for) and hands it to every future session automatically. Think data catalog, minus the platform, the ingestion pipeline, and the invoice. Where amnesic fits ↓

Teach your AI a status code once, then query it in plain language forever. Your AI agent calls amnesic's tools; amnesic remembers across sessions.

Works with Claude Code Β· Claude Desktop Β· Cursor Β· VS Code Β· Cline Β· Windsurf β€” any MCP-compatible client.

Available on Official MCP Registry Β· Claude Code plugin marketplace

πŸ‘‹ Using amnesic? Say hi in the adopters thread β€” download counts can't tell me what's actually being used, and it directly shapes what gets built next.

πŸ”’ Read-only by design. amnesic refuses to execute INSERT, UPDATE, DELETE, DROP, TRUNCATE, ALTER, CREATE, EXEC, MERGE, GRANT, REVOKE β€” 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 ↓


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 β€” one per database β€” 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.

The knowledge is also portable and outlives your access to the database. When you rotate off a project, amnesic export hands the next developer everything you taught it β€” years of "oh, that column actually means…" that would otherwise leave with you.


Where amnesic fits

The database MCP ecosystem splits into two camps, and amnesic is deliberately in neither.

Query executors β€” DBHub, Postgres MCP Pro, Google's MCP Toolbox, and the vendor servers (Supabase, Neon). They introspect live, run SQL, and some go deep on performance β€” Postgres MCP Pro does genuine index tuning and PgHero-style health checks. They are excellent at this. They are also stateless: every session re-learns your schema from scratch, and nothing they return can tell you what a column means, because the database doesn't know either.

Enterprise catalogs β€” DataHub, Atlan, Cube, AtScale. These do hold semantic context: glossaries, column descriptions, ownership, lineage. They're also a platform commitment β€” metadata ingestion, a service to run, usually a paid tier. Worth it at company scale; wildly disproportionate for one developer who needs to remember what six status codes mean in a legacy MSSQL database nobody will ever onboard to a catalog.

amnesic is the third thing: catalog-grade semantic memory at query-executor setup cost. pipx install, one TOML file, a local SQLite file per database. No platform, no ingestion, no server to run.

Honest comparison

amnesicQuery executorsEnterprise catalogs
Semantic context (what a value means)βœ… persistent, yours❌ noneβœ… platform-managed
Survives across sessionsβœ…βŒβœ…
Portable / outlives DB accessβœ… export/import❌⚠️ platform-bound
Setup costone commandone commandingestion pipeline
Live schema freshness⚠️ cached, manual refreshβœ… always live⚠️ ingestion lag
Execution plans / index tuningβŒβœ… (Postgres MCP Pro)❌
Lineage / ownership / governanceβŒβŒβœ…
Works on legacy schemas with no FK constraintsβœ… annotate them yourself❌ nothing to introspect⚠️ needs ingestion

Use a query executor instead of amnesic if you want execution plans, index recommendations, or database health diagnostics β€” that's not amnesic's job and adding it would make it a worse version of a tool that already exists.

Use amnesic alongside one. They compose: nothing stops you running both. amnesic holds the meaning; they hold the machinery.

The rows marked ⚠️ above are known gaps with open issues β€” see Roadmap ↓.


Quickstart (90 seconds)

bash
pipx install amnesic            # install the core
amnesic init                    # interactive wizard

⚑ Try it without credentials. Run amnesic init --demo instead β€” 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 β€” 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 and restart.

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

Install pipx (one-time):

bash
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 β€” fast, no Python required):

bash
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):

Terminal
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 β€” no need to commit to extras up front.

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

Where amnesic stores things

FilemacOS / LinuxWindows
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

bash
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 β€” 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 β€” IT rotated it, you mistyped it during setup, or you're hand-editing the config.

bash
$ amnesic set-secret ORDERS_PROD_PASSWORD
Value: ****            ← hidden input (your typing is invisible)
Confirm: ****
βœ“ 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 nameGenerated env var
orders.prodORDERS_PROD_PASSWORD
analyticsANALYTICS_PASSWORD
drive.stagingDRIVE_STAGING_PASSWORD

To see the exact name your config uses, check ~/.config/amnesic/connections.toml β€” 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:

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

GitHub stars
3
Stargazers on the source repository.
npm downloads
76
Package downloads in the last 30 days.
Last commit
10d ago
Most recent push to the default branch.
Directory activity
2 views
Config copies, upvotes, and views on AllMCPs.

Reviews

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Frequently Asked Questions about Amnesic

It supports PostgreSQL, MySQL, MSSQL, and SQLite.

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Technical Specs & Signals

CategoryπŸ—„οΈDatabases
PricingFree
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimePython
AuthOther
LicenseMIT
ClientsClaude Desktop, Cursor, Windsurf, Cline / VS Code
Last updatedAug 31, 2026
5/6 checks healthy over the last 31d
Views2
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars3
GitHub Star CountTotal stargazers on GitHub representing community popularity (3 stars).
Last commit10d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Aug 31, 2026
npm downloads76/mo
Monthly npm DownloadsAverage monthly package installs recorded from npm registry statistics.
55Quality signal: Good Β· 55/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools24/30
Adoption & activity7/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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Critical 0High 0Medium 0Low 0

Scanned 21d ago via OSV.dev Β· caches (PyPI)

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