"Understanding Attack Surface Management" matching MCP servers:

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.

An MCP server that gives AI agents access to the full Cumulocity API surface through a compact code-mode interface.

An MCP server that exposes Python debugging tools backed by debugpy, providing a focused debugging surface for local scripts.

An MCP server implementing AI-user confirmation protocols, providing tools for LLMs to seek user confirmation when uncertain through yes/no questions, action confirmations, intent clarification, understanding verification, and satisfaction ratings.

MCP server for agentic interaction with the Lean theorem prover via LSP, providing tools for understanding, analyzing, and interacting with Lean projects.

Provides AI assistants with deep semantic and runtime understanding of Jupyter notebooks.

A proof-of-concept attack that exploits Model Context Protocol (MCP) tool registration to achieve persistent agent poisoning in AI assistants like Cursor, embedding malicious instructions that persist across chat contexts without requiring tool execution.

Enables AI assistants to deeply integrate with RStudio for environment management, code execution, project management, and package management.

An intelligent codebase processing server that provides agentic RAG capabilities for code repositories, enabling semantic search and contextual understanding through self-evaluating retrieval loops.

Kody is an MCP server that gives AI agents a portable home for memory, keys, code, and automations on Cloudflare Workers. It offers a compact tool surface with powerful search and execute flows for multi-user personal assistant capabilities.

A server that enables AI assistants like Claude to safely run Python code and access websites, processing data for better AI understanding while providing helpful error messages.

Exposes a stdio JSON-RPC tool surface that lets AI agents control running Flutter and Dart apps — starting, stopping, reloading, and hot-restarting sessions, reading logs, running doctor/list/tinker checks, and invoking plugin-contributed tools. It gives agents one shared protocol instead of ad hoc shell scripts.

A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK, with example implementations of calculator and greeting tools plus resource management capabilities.

A demonstration tool that showcases potential security attack vectors against Model Control Protocol, illustrating how malicious plugins can exploit weaknesses in MCP architecture.

Runs a language server and provides tools for communicating with it. Language servers excel at tasks that LLMs often struggle with, such as precisely understanding types, understanding relationships, and providing accurate symbol references.

A boilerplate project for quickly developing Model Context Protocol servers using TypeScript SDK, with example implementations of calculator tools, greeting functionality, and resource management.

A platform that transforms AI development with intelligent context management, optimization, and prompt engineering, enabling developers to enhance model performance through structured context management and optimization tools.

A Python-based implementation of the Model Context Protocol that enables communication between a model context management server and client through a request-response architecture.

Enables AI agents to execute Jupyter notebook cells with persistent kernel state, output persistence, and structured JSON control surface.

A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK, with example implementations of calculator tools, greeting functions, and resource management.