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

Enables AI agents to auto-load a persistent skill library at session start, and provides proxy tools for MiniMax vision and web search that work around broken MCP stdio transports.

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 capturing technical knowledge from AI development sessions into vetted documents and committing them to a designated private GitHub repository, while supporting search, challenge, and maintenance of the accumulated knowledge base.

Enables MCP-compatible agents to query structured penetration testing engagement state captured from tmux sessions, including credentials, hosts, and command provenance with exact source bytes.

A server that manages conversation context for LLM interactions, storing recent prompts and providing relevant context for each user via REST API endpoints.

Structures debugging processes into a persistent knowledge graph, enabling problem decomposition, hypothesis testing, and reusable solution discovery.

Provides a persistent memory and governance layer that allows AI coding agents to query documented architecture rules and validate code against team standards. It enables agents to verify compliance across categories like security and testing before suggesting changes to ensure consistency across development sessions.

Digital identity layer for AI — your bio, career, skills, interests, and projects always available to every AI tool. Auto-generates profile from 342+ public APIs, 13 real-time plugins, YAML-based profiles with privacy-first local storage.

MCP server for Obsidian vault structural health checks. Find broken wikilinks, orphaned notes, and missing frontmatter, with optional auto-repair.

Persistent project context for Google Gemini. 12 MCP tools for .faf Project DNA — auto-detect your stack, validate, score, and sync across CLAUDE.md, GEMINI.md, and AGENTS.md. Python/FastMCP. IANA-registered format (application/vnd.faf+yaml). 183 tests. One file, every AI platform.

Exposes any project wiki to AI assistants with features like fuzzy search, content search, lazy loading, and auto-reload.

Exposes the MITRE ATT\&CK framework to LLMs and AI assistants via the Model Context Protocol, enabling querying of techniques, tactics, groups, software, and mitigations.

Project-scoped semantic workspace memory for AI coding assistants. Watches your project files and auto-indexes code and docs into Qdrant using tree-sitter semantic chunking and hybrid search (dense + sparse + RRF)

Enables AI agents to build and interact with mind maps on mindmap.io. Agents can create, read, update, and delete maps and nodes, run prompts on nodes, and auto-expand topics into follow-up questions.

Framework-aware code intelligence MCP server that builds a cross-language dependency graph from source code. 53 integrations (Laravel, Django, Rails, Spring, NestJS, Next.js, and more) across 68 languages. 100+ tools for navigation, impact analysis, refactoring, security scanning, session memory, and CI/PR reports — up to 97% token reduction.

Enables multi-dimensional architecture review of technical solutions with web intelligence and knowledge base auto-sedimentation, acting as a logical security gateway for Claude Code.

Persistent cloud memory for AI coding assistants. 28 MCP tools for semantic search, auto-learning, task tracking, correction patterns, knowledge graphs, and session replay across Claude Code, Cursor, Windsurf, Cline, and any MCP client. Encrypted at rest. Team shared memory with author attribution.

Auto-captures decision context from multi-agent workflows to preserve the 'why' behind every choice. Enables task traceability, reasoning retrieval, and continuous improvement across planning and implementation sessions.

Provides access to the HUMMBL Base120 framework of 120 validated mental models organized across 6 transformations, enabling users to search, retrieve, and get AI-recommended mental models for problem-solving and decision-making.

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