ZettelForge runs on your own machine — the client starts it, so there is no endpoint to ping. 89 installs a week from pypi. Last commit 10 Jul 2026.
Agentic memory for cyber threat intelligence. STIX graphs, actor aliasing, offline RAG, Sigma/YARA.
Every tool that appeared, vanished or quietly changed what it asks for. Recorded since 9 August 2026. No other catalogue keeps this.
We read the source, 1 d ago · rules 3dff92dd89df
What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.
status.innerHTML = html;
Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.
We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.
This server runs on your own machine — install it with the package manager and the client starts it for you. Package name taken from the official registry entry.
claude mcp add zettelforge -- uvx zettelforge
{
"mcpServers": {
"zettelforge": {
"args": [
"zettelforge"
],
"command": "uvx"
}
}
}
[mcp_servers.zettelforge]
command = "uvx"
args = ["zettelforge"]
{
"mcpServers": {
"zettelforge": {
"args": [
"zettelforge"
],
"command": "uvx"
}
}
}
{
"mcpServers": {
"zettelforge": {
"args": [
"zettelforge"
],
"command": "uvx"
}
}
}
Repository intelligence, code graph, history, papertrail, and cross-agent memory for coding agents.
Offline agentic memory: remember/recall/relate/forget/why over a fused vector+graph+columnar engine
Two-layer memory MCP server for AI agents with 37 tools, RAG, graphs, wiki, auth
Deep code indexing for AI agents. FTS5 + embeddings + call graphs + git intelligence. Fully local.
Graph + vector memory for agents: recall, ingest, search, distill. Local or remote backend.
Cognitive memory for AI agents. Atkinson-Shiffrin, RAG, knowledge graph. 100+ tools.
Offline codebase knowledge graph: 91% token reduction vs naive RAG, plus cross-session agent memory.
RAG memory for LLM agents over Garnet Vector Sets: store text as embeddings, recall by meaning.
Answers built from our own checks of this server.