"A management control platform (MCP) compatible with the Cypress framework" matching MCP servers:

AI-native orchestration layer with 80+ tools for task management, code editing, browser automation, terminal control, and persistent memory across CLI, local MCP, and cloud deployments.

A lightweight and modular framework for building MCP-based file management systems with standardized module structure, flexible configuration, and extensible functionality registration mechanisms.

A TypeScript framework for building Model Context Protocol (MCP) servers with automatic discovery and loading of tools, resources, and prompts.

A communication framework that enables multiple AI agents to collaborate through asynchronous messaging, agent registration, and discovery. It facilitates complex task coordination and real-time discussion across distributed agent workflows using the Model Context Protocol.

A powerful Model Context Protocol framework that extends Cursor IDE with tools for web content retrieval, PDF processing, and Word document parsing.

A framework for building MCP servers with standardized tools, authentication, logging, and multiple transports. Enables rapid development of secure, auditable MCP tools.

An MCP server framework enabling standardized tool registration, discovery, and invocation over JSON-RPC 2.0, with support for InProcess, SSE, and WebSocket transports, plus built-in tools such as calculator, text processor, and JSON formatter.

MCP server for controlling satellite ground station operations in the Trench simulation, including pass management, downlink control, and antenna pointing.

Enables AI assistants to interact with the Open Targets Platform API to search and retrieve comprehensive data about target-disease associations, drugs, genes, and drug discovery information through GraphQL queries.

Lets your coding agent talk to the RunWhen platform — workspace chat, issues, SLXs, run sessions, and the Tool Builder — over the Model Context Protocol. Enables workspace chat with AI assistant, task authoring via Tool Builder, and direct data access to workspaces, issues, SLXs, run sessions, and more.

A governance and control layer for MCP tools that manages tool requests as intents through policy-based approval, queuing, or blocking. It enables secure human oversight and audit trails for consequential agent actions across platforms like Claude Desktop and Cursor.

Provides specialized tools for portfolio health analysis, rebalance simulations, and trade execution via a single interface powered by MongoDB. It enables AI agents to manage investment portfolios while adhering to organizational governing rules, clearance guards, and rate limits.

A shared communication bus for multiple agents using NATS and MCP, enabling MCP clients to list, message, and query agents.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

An advanced management platform that integrates FastAPI and React TypeScript, providing a modern user interface with SQLite database and action history tracking.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

Start, observe, and interact with Claude Managed Agents from any MCP client — launch an agent, watch its events, reply, approve the tools it wants to run, and stop it. Runs over stdio, HTTP, or AWS Lambda with pluggable auth.

Manage Job using MCP: Manage Job, Candidates, Resumes, Salaries all within this one MCP tools It can solve problems like: You have 50 resumes to screen. Your AI assistant can reason about candidates, but it can't: Read PDFs/DOCX — The AI can't open binary files Extract structured data — Copy-pasting loses formatting, metrics, and context Compare at scale — No consistent scoring across candida

Contract-driven test enforcement and reporting for LLM-generated code via MCP, VS Code, Copilot CLI, Claude, Cursor, or Python SDK.

Enables MCP hosts like ChatGPT and Claude to query data platform metadata, explore schemas and tables, inspect SQL explain plans, and monitor DAG and ETL status through standardized tools.