"Understanding Attack Surface Management" matching MCP servers:

Enables AI agents to query a repository's verified semantic model, including capabilities, ownership, contracts, constraints, impact, health, and verification status, without re-deriving project facts. It exposes read-only tools for inspecting, reasoning about, and verifying the provenance-tracked surface.

An MCP server that analyzes local or remote GitHub repositories, providing intelligent code context and structure to AI coding assistants.

Enables AI assistants to manage project analysis, code metrics, documentation, Git operations, code quality, and file organization through natural language commands.

Provides AI coding agents with five intelligence layers (dependency graph, git history, documentation, architectural decisions, code health) via nine MCP tools, enabling deep codebase understanding and reducing exploration cost.

Offers a control surface for AI agent security with four independent checks: source-code scanning, tenant isolation, LLM content verification, and deploy gating. Currently in pre-release, with no functional scanning engines yet.

An MCP server that helps AI agents comprehend a codebase by providing tools for navigating, searching, and understanding code structure and history.

MCP security server for AI coding agents. 12 tools: pre-install guardian, vulnerability audit, supply-chain attack detection via static code analysis, and CycloneDX 1.6 SBOM generation. Zero runtime dependencies.

A self-contained, in-memory graph database for AI Agents. Provides semantic code understanding through the Model Context Protocol (MCP).

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.

An MCP server that gives Claude structural understanding of a codebase—dependency graph, entry points, complexity ranking, and import cycles—without reading every file into context.

Local MCP tool for understanding JSON Schema files while planning tests, summarizing types, required fields, constraints, and generating placeholder paths without copying data.

Stop pasting your file tree into Claude. Give any AI assistant real architectural understanding of a codebase — local, private, zero‑config.

Integrates Google Gemini 3.1 with Claude Code to provide web search, large codebase analysis, multimodal image understanding, and creative brainstorming, leveraging Gemini's 1M token context and Google Search grounding.

Enables AI-assisted FlutterFlow development by reading, exploring, and understanding FlutterFlow projects through Claude and other MCP-compatible clients.

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.

Provides structural, queryable understanding of a Python codebase via MCP tools, enabling direct lookups for callers, dependencies, and class hierarchies without repeated grep/read cycles.

A local semantic memory and code-indexer that uses AST parsing for structural understanding and persists architectural decisions to help AI assistants bypass context window limits.

An extensible MCP server that provides intelligent Python code analysis, navigation, and understanding capabilities for AI assistants like Claude.

A security audit server for Laravel projects that performs static code analysis, dependency CVE checks, and configuration audits. It enables developers to detect vulnerabilities like SQL injection and XSS while providing active attack simulations directly within MCP-compatible IDEs.

MCP server that exposes llm-tldr code analysis tools (tree, structure, context, search, impact, etc.) to MCP clients like Claude Code for project understanding and debugging.