AI agent security: prompt injection detection, semantic memory, output scanning, prompt hardening
Agent MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. AI agent security: prompt injection detection, semantic memory, output scanning, prompt hardening.
Adaptive threat intelligence for AI agent security: semantic memory, multi-turn escalation, output scanning, rate limiting, and prompt hardening — designed to complement deterministic governance stacks (e.g. Microsoft Agent OS), not replace them.
Once Agent is connected, these are the calls the assistant has available:
Transport — When to usesse — HTTP clients that expect the legacy SSE MCP transport (--port binds 127.0.0.1)Capability — Rule-only (typical)Dataset — RowsCombined — 823Stage — LearnedScript — What it showsProject — FocusAgentSeal — Red-team / MCP auditObservability — metrics = MetricsCollector() immune = AdaptiveImmuneSystem(metrics=metrics) immune.assess("some text") print(metrics.snapshot())agent-immune on PyPI is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Among the monitoring and observability options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Agent's toolset — Transport, sse, Capability and 7 more — is a fair guide to whether it matches your workflow. It is maintained by denial-web; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| Transport | When to use |
| sse | HTTP clients that expect the legacy SSE MCP transport (--port binds 127.0.0.1). |
| Capability | Rule-only (typical) |
| Dataset | Rows |
| Combined | 823 |
| Stage | Learned |
| Script | What it shows |
| Project | Focus |
| AgentSeal | Red-team / MCP audit |
| Observability | metrics = MetricsCollector() immune = AdaptiveImmuneSystem(metrics=metrics) immune.assess("some text") print(metrics.snapshot()) |
{
"mcpServers": {
"agent-immune": {
"command": "uvx",
"args": ["agent-immune"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
Give your coding agent the full DevTools toolbox: traces, network, console, heap snapshots and Lighthouse.
Dashboards, Prometheus and Loki queries, incidents and alerts — observability by conversation.
Errors with full context — stack traces, issue triage and AI-powered root-cause analysis from Sentry's server.
Enables enhanced web research capabilities for large language models through intelligent search queuing and advanced content extraction.
Automates browser interactions and enables Large Language Models (LLMs) to interact with web pages through Playwright and Chrome DevTools Protocol
Guides tool usage by providing recommendations for MCP tools at each problem-solving stage.