"Villager 0.2.1rc1 Framework MCP Auto Penetration Testing" matching MCP servers:

A framework for building MCP servers with built-in enterprise capabilities such as authentication, authorization, telemetry, audit logging, and data classification, allowing domain developers to focus on business logic while maintaining governance and compliance.

Runtime constitutional verification for AI answers — claim extraction with reasoning chains, Epistemic Confidence Score (ECS), 7-angle Glassbox Court red team, constitution compilation, Trust Card assembly, and deterministic SHA-256 audit logs.

The policy enforcement plane for MCP: deterministic admission and egress policy, attributable audit, and SIEM export for your MCP server fleet. Self-hosted and MIT licensed.

Enables building, validating, describing, and conformance-testing TTEOP telemetry envelopes through the Model Context Protocol.

A Python MCP server that reduces token usage by ~98% when working with log files by auto-detecting format and stripping noise to return only actionable signal.

MCP server that auto-emits tamper-evident receipts for every tool call, enabling EU AI Act Article 12 compliance with signed, chain-linked receipts.

An MCP server that enables AI agents to safely operate multi-cluster Kubernetes fleets through an Open Cluster Management hub, with policy enforcement, human approval, and audit tracing.

A Model Context Protocol server providing network diagnostic tools for AI agents, including connectivity testing, batch operations, local network info, and pcap analysis.

Zero-config MCP server that gives AI coding assistants a real-time diagnostic snapshot of your local dev environment. Detects framework, running services, recent errors, git state, and provides a health diagnosis in one call.

Сonnects to live JVMs via JDWP protocol, enabling LLM agents to autonomously debug Java applications — attach to a running process, pause threads, set breakpoints (including conditional), inspect stacks with auto-resolved object fields, evaluate methods, set variable values, and diagnose issues like deadlocks and hung queries.

A specialized diagnostic framework that enables AI models to self-diagnose and fix MCP-related issues by analyzing logs, validating configurations, testing connections, and implementing solutions.

Seamlessly bring real-time production context—logs, metrics, and traces—into your local environment to auto-fix code faster.

Enables interaction with Sentry's error tracking and monitoring tools through a unified API, allowing natural language access to Sentry services.

Lets AI agents diagnose and debug live Java applications by auto-discovering JVMs and attaching Arthas to run diagnostic commands like thread, trace, watch, and ognl.

A chaos-engineering and resilience-testing toolkit for Model Context Protocol servers, enabling deterministic fault injection and testing of reliability scenarios.

Remote MCP server enabling AI agents to manage the MaxoPerf performance testing platform via natural language, including creating tests, launching cloud load runs, viewing results, and diagnosing failures.

Enables AI assistants to analyze Windows WPR/ETW traces (.etl files) using natural language queries, with features like auto-summary, CPU sampling, DPC/ISR analysis, and symbol resolution.

MCP server for monitoring, diagnosing, and auto-correcting observability issues across platform services, integrating with Sentry and PostgreSQL for error tracking and billing pipeline health.

Enables natural language Kubernetes operations, including smart resource queries, pod root-cause analysis, cross-environment diffs, and manifest generation.

Enables interaction with Prometheus Alertmanager for querying alerts, managing silences, and investigating incidents. Supports direct API connection and Kubernetes auto-discovery.