Dingdawg Loop runs on your own machine — the client starts it, so there is no endpoint to ping. 45 installs a week from npm. Last commit 15 Jul 2026.
Safe scheduled AI agents with governance gates. Verified, receipted, fail-closed.
We read the source, 23 h 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.
const files = fs.readdirSync(LOOPS_DIR).filter((f) => f.endsWith(".json") && !fs.statSync(path.join(LOOPS_DIR, f)).isDirectory());
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 dingdawg-loop -- npx -y dingdawg-loop
{
"mcpServers": {
"dingdawg-loop": {
"args": [
"-y",
"dingdawg-loop"
],
"command": "npx"
}
}
}
[mcp_servers.dingdawg-loop]
command = "npx"
args = ["-y", "dingdawg-loop"]
{
"mcpServers": {
"dingdawg-loop": {
"args": [
"-y",
"dingdawg-loop"
],
"command": "npx"
}
}
}
{
"mcpServers": {
"dingdawg-loop": {
"args": [
"-y",
"dingdawg-loop"
],
"command": "npx"
}
}
}
This one needs environment variables set before it will start:
DINGDAWG_API_KEY (API key for paid tier access — get free at dingdawg.com).
The author declared them in the registry entry; get the values from the project itself.
Fail-closed verify-before-you-act gate for AI agents. Signed receipts. Pay-per-call via x402.
Fail-closed agentic OS MCP: agents as processes with cost, governance, observability, skills.
Governance layer for agentic AI — signed, verifiable receipts for every agent action.
Local recursive self-improvement OS MCP with fail-closed governance and gated mutation tools.
Fail-closed quality gate and hash-chained receipt ledger for AI agent workflows.
Financial governance for AI agents — spend gates and audit trail exposed as MCP tools.
Runtime verification MCP server: fail-closed checks for agent tool calls, signed evidence receipts.
Verified memory for coding agents: claims cited against code, stale withheld, savings receipts.
Answers built from our own checks of this server.