"How to Assess the Security of a Python Application" matching MCP servers:

Enables secure interaction between LLMs and MCP tools by applying zero-trust security controls, including sensitive data masking, file system protection, and policy enforcement.

Enables transparent security for any MCP server by intercepting tool calls, blocking prompt injection attempts, masking PII in responses, and writing immutable audit logs.

MCP server for safely exposing SQL Server database capabilities to LLM clients, with read-only mode, security features, and observability.

A Model Context Protocol (MCP) server for comprehensive monitoring and observability of systems using Langfuse.

Enables AI assistants to monitor and troubleshoot AWS Application Signals services by tracking service health, analyzing SLO compliance, querying CloudWatch metrics, and investigating issues using distributed tracing with AWS X-Ray.

MITM proxy MCP server that intercepts and verifies AI agent tool calls, detecting fabrication, tracking costs, verifying outcomes, and inferring user satisfaction.

A verifying MCP gateway that pins approved tool manifests and skill files to content addresses, blocking tampered descriptions before they reach model context.

An MCP server for Security Onion Community Edition that provides 21 read and write tools for SOC operations, including event querying, alert management, detection tuning, PCAP retrieval, and agent enrollment, without requiring a Pro license.

Provides MCP tools to create and query SigNoz trace funnels, enabling agents to measure their own step conversion rates from within the agent loop.

An MCP server that provides cost and reliability observability for LLM and agent workflows. It records model calls and allows querying and aggregating telemetry data through MCP tools.

Fetches Langfuse observability traces directly into a VS Code coding agent's context, enabling querying and viewing trace data through natural language.

Gives AI assistants access to F6 XDR alerts, incidents, emails, files, hosts, and audit logs via query-language search, with tools for counting records, inspecting available fields and filters, and marking events.

Enables AI agents to monitor Docker containers, system resources, ports, and logs in real-time.

Enables natural language interaction with Plausible Analytics data to query traffic, visitors, engagement, and more using conversational questions.

Enables developers to build production-grade MCP services with dependency injection, zero global state, layered imports, and a sample user-notes tool, resource, and prompt. It includes Testcontainers, Prometheus metrics, Kubernetes probes, and structured logging for testable local and production deployments.

Enables Windows Event Log (EVTX) analysis by wrapping Hayabusa, exposing scan and rule retrieval tools.

Enables secure MCP traffic by filtering threats with a 3-stage detection pipeline, applying RBAC policies, and redacting sensitive data in real-time via an SSE-inspecting proxy.

Enables remote log searching, tailing, and file reading across servers via MCP tools, with pluggable execution via SSH, pyinfra, or local commands.

Read-only MCP server that surfaces UniFi network data from a UCG-Fiber console to Claude, enabling monitoring of hosts, sites, devices, clients, ISP metrics, and device stats via read-only API tools.

BeamScope gives AI coding agents access to your running BEAM application through a resilient TCP architecture that survives app restarts.