Hot Memory MCP

Name: Hot Memory MCP
Author: michael-denyer

by michael-denyerversion 0.8.0i★ –iRuns on your computer

Registry nameio.github.michael-denyer/hot-memory-mcp

Install

Overview

Description from the maker (original English)

Two-tier memory: hot cache (0ms) + semantic search. Self-organizing.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: MEMORY_MCP_DB_PATH, MEMORY_MCP_EMBEDDING_MODEL

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "hot-memory": {
      "command": "uvx",
      "args": [
        "hot-memory-mcp"
      ],
      "env": {
        "MEMORY_MCP_DB_PATH": "your-value",
        "MEMORY_MCP_EMBEDDING_MODEL": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add hot-memory --env MEMORY_MCP_DB_PATH=your-value --env MEMORY_MCP_EMBEDDING_MODEL=your-value -- uvx hot-memory-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "hot-memory": {
      "command": "uvx",
      "args": [
        "hot-memory-mcp"
      ],
      "env": {
        "MEMORY_MCP_DB_PATH": "your-value",
        "MEMORY_MCP_EMBEDDING_MODEL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "hot-memory": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "hot-memory-mcp"
      ],
      "env": {
        "MEMORY_MCP_DB_PATH": "your-value",
        "MEMORY_MCP_EMBEDDING_MODEL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

MEMORY_MCP_DB_PATH env
Path to SQLite database file
MEMORY_MCP_EMBEDDING_MODEL env
Embedding model to use (default: all-MiniLM-L6-v2)

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account michael-denyer, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 5654 servers in this category
Published to the registry more recently than 20% of the other 5654 servers in this category
Basic checks equal to or better than 100% of the other 5654 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Hot Memory MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.8.0) reached the registry 4 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 2 months ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

🧠 Memory MCP

Give your AI assistant a persistent second brain

Stop re-explaining your project every session.

Memory MCP learns what matters and keeps it ready — instant recall for the stuff you use most, semantic search for everything else.

The Problem

Every new chat starts from scratch. You explain your architecture *again*. You paste the same patterns *again*. Your context window bloats with repetition.

Other memory solutions help, but they still require tool calls for every lookup — adding latency and eating into Claude's thinking budget.

Memory MCP fixes this with a two-tier architecture:

Hot cache (0ms) — Frequently-used knowledge printed into context by the plugin's SessionStart and UserPromptSubmit hooks *before Claude even starts thinking*. Claude Code adds a hook's stdout to the conversation, so no tool call is needed.
Cold storage (~50ms) — Everything else, searchable by meaning via semantic similarity.

The system learns what you use and promotes it automatically. Your most valuable knowledge becomes instantly available. No manual curation required.

Before & After

😤 Without Memory MCP	🎯 With Memory MCP
"Let me explain our architecture again..."	Project facts persist and isolate per repo
Copy-paste the same patterns every session	Patterns auto-promoted to instant access
500k+ token context windows	Hot cache keeps it lean (~20 items)
Tool call latency on every memory lookup	Hot cache: 0ms — a hook already put it in context
Stale information lingers forever	Trust scoring demotes outdated facts
Flat list of disconnected facts	Knowledge graph connects related concepts

Install

# Install package

uv tool install hot-memory-mcp # or: pip install hot-memory-mcp

Add plugin (recommended)

claude plugins add michael-denyer/memory-mcp

The plugin gives you auto-configured hooks and slash commands. Embeddings run on sentence-transformers on every platform, including Apple Silicon.

Manual config (no plugin)

Add to ~/.claude.json:

"mcpServers": { "memory": { "command": "memory-mcp" } } }

See Reference for full configuration options.

Restart Claude Code. Run memory-mcp-cli bootstrap once to seed memories from your project docs; the hooks inject whatever is promoted, but nothing seeds itself.

First run: Embedding model (~90MB) downloads automatically. Takes 30-60 seconds once.

How It Works

flowchart LR

subgraph LLM["Claude"] REQ((Request)) end

subgraph Hot["HOT CACHE · 0ms"] HC[Session context] PM[(Promoted memories)] end

subgraph Cold["COLD STORAGE · ~50ms"] VS[(Vector search)] KG[(Knowledge graph)] end

The hot cache (~10 items) reaches Claude through two plugin hooks. SessionStart runs memory-mcp-cli hot-cache --force and UserPromptSubmit runs memory-mcp-cli hot-cache, and Claude Code adds each command's stdout to the conversation. The command prints only when the text differs from what the session last saw, which is cheap while the set is stable and reprints in full when it shifts. The set combines recent recalls, predicted next memories, and top promoted items. Promoted memories (~20 items) is the backing store of frequently-used memories. Memories used 3+ times auto-promote; unused ones demote after 14 days.

What Makes It Different

Most memory systems make you pay a tool-call tax on every lookup. Memory MCP's hot cache bypasses this entirely — a hook prints your most-used knowledge into the conversation before Claude starts thinking.

	Memory MCP	Generic Memory Servers
Hot cache	Injected by a hook at 0ms	Every lookup = tool call
Self-organizing	Learns and promotes automatically	Manual curation required
Project-aware	Auto-isolates by git repo	One big pile of memories
Knowledge graph	Multi-hop recall across concepts	Flat list of facts
Trust scoring	Outdated info decays and sinks	All memories equal
Setup	One command, local SQLite	Often needs cloud setup

The Engram Insight: Human memory doesn't search — frequently-used patterns are *already there*. That's what hot cache does for Claude.

Quick Reference

Slash Command	Tool	Description
`/memory-mcp:remember`	`remember`	Store a memory with semantic embedding
`/memory-mcp:recall`	`recall`	Search memories by meaning
`/memory-mcp:hot-cache`	`promote` / `demote`	Manage promoted memories
`/memory-mcp:stats`	`memory_stats`	Show statistics
`/memory-mcp:bootstrap`	`bootstrap_project`	Seed from project docs
—	`link_memories`	Knowledge graph connections

See Reference for all 10 slash commands and full tool API.

Dashboard

memory-mcp-cli dashboard    # Opens at http://localhost:8765

Browse memories, hot cache, injection history, sessions, and the knowledge graph.

How to Use

Memory MCP is designed to run as three complementary components:

Component	Purpose
Claude Code Plugin	Hooks that inject the hot cache, plus the `/memory-mcp:*` slash commands
MCP Server	Core memory tools available to Claude via Model Context Protocol
Dashboard	Web UI to browse, manage, and debug your memory database

The plugin is recommended for most users — it auto-configures the MCP server and adds productivity features. Run the dashboard alongside when you want visibility into what's being stored.

Documentation

Document	Description
Reference	Full API, CLI, configuration, MCP resources
Troubleshooting	Common issues and solutions

License

MIT

Hot Memory MCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

🧠 Memory MCP

Give your AI assistant a persistent second brain

The Problem

Before & After

Install

Add plugin (recommended)

How It Works

What Makes It Different

Quick Reference

Dashboard

How to Use

Documentation

License