"Understanding Attack Surface Management" matching MCP servers:

Enables MCP-compatible AI assistants to scan files and raw content for malware, prompt injection, and other threats, manage accounts and API keys, and access SDK/API documentation.

Enables cyber defenders to query ATT\&CK techniques, list tactics, map incidents to techniques, look up threat actor groups and mitigations, all via the MCP protocol.

Tracks and manages code changes from Git repositories, generates documentation automatically, and provides AI agents with intelligent access to service documentation through MCP server integration with feature mapping and search capabilities.

This MCP server enables Claude AI to read and edit documents through natural language, leveraging tools, resources, and prompts for document management in a Ruby-based CLI application.

Provides tools to authenticate, search, and manage sessions within WingArc's iA Document Management System. It enables users to perform free word document searches and manage security tokens through the Model Context Protocol.

Enables querying the MITRE ATT\&CK framework for adversarial tactics, techniques, mitigations, and detection methods through natural language, supporting both ID-based and fuzzy name-based searches.

Enables Claude AI to perform comprehensive file operations including reading, writing, notes management, PDF processing, and image handling with thread-safe operations. Provides a complete file management system with enhanced features like document processing and automatic directory creation.

A secure, open-source Model Context Protocol server for connecting AI agents and apps to Notion. It enables search, read, create, and append operations on Notion pages with audit logging and risk-based security.

Code-mode MCP server (docs_search + execute_code two-tool surface) backed by a unified capability manifest across three sandbox kernels (in-process node:vm, WASM via QuickJS / Pyodide / Wasmtime, and remote microVM via E2B / Cloudflare Sandbox). At N=30 tools the bootstrap-context cost drops to 13.6% of direct tool-use. Apache-2.0.

Connects AI agents with Swagger/Knife4j API documentation, enabling real-time retrieval, search, and structured understanding of API interfaces, parameters, and schemas.

Provides the real exported API surface and type signatures of any npm package, extracted from its published TypeScript declarations, enabling AI coding agents to use functions accurately and avoid inventing nonexistent APIs.

Provides read-only access to Gemini 3 Online's knowledge surface (models, pricing, links, FAQ) for MCP-compatible AI clients, requiring no API keys.

High-performance code understanding toolkit that enables batch reading of multiple files with dependency context, structural outline extraction with Java annotation awareness, and precise location of classes/methods across large codebases.

MCP server that indexes Markdown, Word, HTML, and PDF documents into a SQLite knowledge graph with CJK+Latin full-text search and cross-document reference tracking. Runs drift audits to surface stale policies, conflicting research claims, superseded ADRs, and undocumented code exports.

MCP server that provides AI coding agents automatic access to AGENTS.md documentation from GitHub repositories, enabling understanding of codebase conventions and patterns.

A Model Context Protocol server for Confluence Cloud that aggressively trims API responses to reduce token usage, with features like disk offload and tunable tool surface.

Assists AI coding agents in understanding and implementing UTCP by providing documentation search, manual validation, code generation, OpenAPI conversion, and an LLM-powered expert agent.

Provides comprehensive access to WCAG 2.2 accessibility guidelines, including all 87 success criteria with full Understanding documentation, 400+ techniques, glossary terms, and ACT test rules from official W3C data.

A Model Context Protocol (MCP) server for structured understanding of RFC documents.

Enables structured document understanding of local PDFs via a dual-extract MCP server, combining MinerU text/layout and Qwen3-VL vision with fusion adjudication for field extraction, tables, formulas, and validation. Fully local and offline.