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

A middleware system that connects large language models (LLMs) with various tool services through an OpenAI-compatible API, enabling enhanced AI assistant capabilities with features like file operations, web browsing, and database management.

基于MCP协议的知识库服务器，内置LLM层，提供结构化知识存储、语义搜索和主动知识推送，支持通过工具进行知识学习、搜索和关联推理。

Enables AI assistants to access and manage an Indonesian stock market news pipeline, including checking news status, running scraping, and reading reports.

A tool that helps easily register Anthropic's Model Context Protocol (MCP) in Claude Desktop and Cursor, providing RAG functionality, Dify integration, and web search capabilities.

A tool that helps users easily register Anthropic's Model Context Protocol in Claude Desktop and Cursor, providing ready-made MCP tools for RAG, web search, and Dify integrations.

Provides AI assistants with real-time access to 1000+ AI models including their latest pricing, context windows, capabilities, and specifications. Supports model search, comparison, recommendations, and live testing.

MCP server for the Point Knowledge API that enables AI coding assistants to search and retrieve curated technical documentation (RFCs, framework docs, etc.) with hybrid search and precise citations.

An MCP server that provides LLMs with accurate, version-matched MOOSE framework knowledge to generate valid and idiomatic DCS mission code, with tools for ingestion, lookup, validation, and mission/log parsing.

Provides AI agents with a temporal knowledge graph where knowledge relevance decays over time, enabling memory recall, reinforcement, and auto-archiving through MCP tools.

Provides access to a community knowledge base of 16k+ debugging solutions and 223+ reusable skills, plus auto-scanned project-specific knowledge bases that learn and store solutions as you work.

Integrates AI assistants with the Unity Editor for scene manipulation, asset management, and testing, plus Unity documentation search via RAG.

Enables AI assistants to save, load, and search conversation context with AI-powered summarization and auto-tagging. Demonstrates semantic intent patterns and hexagonal architecture for maintainable AI-assisted development.

Provides pre-defined prompt templates for AI assistants to generate comprehensive plans for TypeScript projects, API architectures, and GitHub workflows.

LLM routing proxy that cuts API costs 60-90% by auto-selecting the cheapest capable model across OpenAI, Anthropic, and Google. Provides stats, config, and model comparison tools via MCP.

Provides local technical documentation with hybrid search (semantic + BM25) for AI agents, ensuring access to up-to-date framework docs.

Knowledge-graph memory server for MCP-compatible AI tools, providing persistent, connected memory with typed relationships and auto-consolidation.

Enables running durable, traceable AI agents via LangGraph through a universal MCP interface, integrating with Hatchet for orchestration, logging, and retries. Provides tools for knowledge management (ingestion, RAG) and Kubernetes operations (diagnosis, auto-fix).

Enables AI applications to use advanced memory management capabilities through the memU AI framework. Supports storing conversation memories, semantic retrieval, multi-user management, and memory statistics via standardized MCP protocol.

Enables MCP host applications to retrieve and process structured documentation from user-defined llms.txt files. It provides tools to fetch specific URLs and audit the documentation context returned to the LLM.

Framework-agnostic MCP server for agent memory with Postgres + pgvector, enabling persistent memory, recall, and task management across sessions.