"A manager for organizing and handling tools" matching MCP servers:

Acts as a personal AI development environment manager, enabling workspace bootstrapping, headless skill and bundle installation, global caching, and health diagnostics.

A gateway that manages multiple MCP servers behind one endpoint, providing namespaced tool routing and supervision. Currently bundles an Arxiv MCP server for paper search and retrieval.

A durable MCP server manager for DeepSeek Harness that provides a settings page and model tools to add, list, enable/disable, restart, import/export MCP servers while surviving DSH restarts and upgrades.

Enables AI agents to control a Playwright-driven browser through natural-language instructions, including page interaction and live browser task execution.

Provides Dify apps with a secure, app-scoped MCP tool catalog and action gateway for authorized FlowFlox operations, enabling intent-driven planning and fixed operational processes without exposing global credentials.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

This MCP server enables agents to manage complex tasks by providing tools for registration, complexity assessment, breakdown into subtasks, and status tracking throughout the task lifecycle.

Enables AI agents to drive real Google Chrome with stealth and captcha handling, using Patchright to avoid bot detection.

MCP server for managing AI agent skills with pre-flight compatibility checks, ensuring missing dependencies are detected before execution.

A deterministic MCP tool-list relay that lets operators filter tools by include/exclude rules and exposes a filtered stdio MCP server to local clients.

This MCP server provides tools to manage, score, compress, and prune AI agent conversation context, helping keep agents focused and reduce token costs. It is a free, local, pure Python solution for any MCP client.

A passive MCP server that exposes a toolbox of executable tools (shell, network, HTTP, AI search, SSH, S3 file operations) to autonomous agents via Streamable HTTP, with strong security features including Docker sandboxing and WAF.

Federates multiple MCP servers into a single endpoint with search, call, and admin tools, plus inbound/outbound OAuth.

Enables centralized management of MCP servers across multiple AI coding harnesses, including adding, removing, monitoring, configuring, and syncing servers with encrypted token storage.

Enables AI agents to securely discover, invoke, and manage tools through a hardened MCP endpoint with protections like injection detection, circuit breakers, retry backoff, response caching, context-window limiting, and state snapshots.

Transforms any API into an intelligent MCP agent in seconds, enabling natural language interaction with APIs through the Model Context Protocol.

Manages resource allocation, MCP server lifecycle, and Kubernetes workers in cortex automation systems. Provides tools for requesting/releasing job resources, starting/stopping/scaling MCP servers, and provisioning/destroying burst workers with TTL management.

Provides context-aware skill selection for AI agents, reducing token usage by 85-98% and improving accuracy through semantic retrieval, session memory, and feedback learning.

Enables users to plan, coordinate, and govern multi-agent teams using MongoDB for shared context, token budgets, and checkpoint recovery.

A meta-MCP server that orchestrates tools from multiple MCP servers, enabling complex Python workflows with loops and conditionals.