"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.

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

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.

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

A minimal guardrails framework for AI agents, acting as an MCP proxy to enforce policies, approvals, and audit logging on tool calls.

FastMCP server for managing and testing webhooks via webhook-test.com API

An MCP server that connects language models to a Wazuh SIEM cluster, enabling read-only plain-language queries, schema inspection, field coverage analysis, and decoder testing.

Open-source .NET framework and server for Model Context Protocol (MCP) with enterprise OAuth 2.0/OIDC auth, per-tool MFA, OpenTelemetry, and health checks.

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

CI health reports for GitHub Actions: flaky tests named from logs, wasted compute priced in dollars, zombie crons, PR feedback time, plus 21 lint rules with safe auto-fixes. Read-only MCP tools wrap the same Go binary.

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

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.

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.

An MCP server that emits OpenTelemetry traces, metrics, and logs to OTLP endpoints in various formats. It includes tool-mimicry profiles to generate realistic traffic resembling common tools like nginx, postgres, and AWS Lambda for testing and demonstration purposes.

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.

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 AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

Universal verifiable recovery for long-running AI agents with semantic checkpoints, idempotent action ledger and hash chained log as a deny by default MCP server. Framework agnostic with adapters for LangGraph, LangChain and OpenAI plus gateway and OTel.

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

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.

Provides a deployable template that turns a Cloudflare Workers account into a hosted MCP endpoint with OAuth-gated private tools, auto-generated docs, health checks, rate limiting, and a server-rendered monitoring dashboard.

Flask Extension for apcore (AI-Perceivable Core) integration. Expose your Flask routes as MCP tools with auto-discovery, Pydantic schema inference, and built-in observability.

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 control a local Clash Verge Rev / mihomo core on Windows over its named-pipe controller, discovering the live pipe automatically so it works in both sidecar and service mode. Supports inspecting status, switching proxy nodes, latency testing, changing modes, viewing connections and rules, and updating providers.

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.

Enables AI assistants to perform chaos engineering and resilience testing on Kubernetes clusters through Chaos Mesh. Supports creating and managing 24 types of chaos experiments including network failures, stress tests, pod disruptions, and I/O faults through natural language conversations.

Enables DNS and email security analysis through passive and active scanning capabilities. Provides comprehensive domain security checks including SPF, DMARC, DNSSEC validation, MX record analysis, and SMTP connectivity testing.

A modular Retrieval-Augmented Generation (RAG) framework that provides hybrid search and knowledge retrieval capabilities via the Model Context Protocol. It enables users to integrate document-based knowledge into LLM workflows with support for dense/sparse retrieval, reranking, and observability.