Security scanner for AI agent tooling — MCP servers, tool definitions, and agentic pipelines
Agent Audit MCP server exists for a simple reason — assistants are far more useful when they can act on Agent Audit directly instead of describing what you should do. Security scanner for AI agent tooling — MCP servers, tool definitions, and agentic pipelines.
Catch prompt injection, command injection, hardcoded secrets, and excessive permissions in your MCP server configurations — before attackers do.
agent-audit on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
You will need 2 environment variables: AWS_ACCESS_KEY_ID, YOUR_API_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. It is maintained by piiiico; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
{
"mcpServers": {
"agent-audit": {
"command": "npx",
"args": ["-y", "agent-audit"],
"env": {
"AWS_ACCESS_KEY_ID": "your-value",
"YOUR_API_KEY": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| AWS_ACCESS_KEY_ID | Credential the server authenticates with. | Yes |
| YOUR_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.