"Official Ads Manager Certification Program (MCP)" matching MCP servers:

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.

Enables self-hosted Google Ads and Meta Ads management through MCP, with human approval, hard spending limits, and audited writes; agents like Claude Code or Codex can connect to ad accounts while the server controls all credential access and platform writes.

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

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.

A comprehensive Google Ads API integration for AI assistants through the Model Context Protocol. This server enables Claude AI and other MCP-compatible clients to manage Google Ads campaigns, analyze performance, and execute GAQL queries with natural language commands.

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

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 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.

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.

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.

Acts as a multi-tenant MCP gateway, letting multiple AI clients access tenant-specific tools and integrations through a single public endpoint, with configuration and secrets managed via YAML and environment variables.

An MCP server that integrates with AI editors like Cursor to maximize agentic capabilities while solving context window limitations, providing a Svelte UI for task planning and implementation tracking.

Enables AI coding agents to self-assign, coordinate, review, and ship work through GitHub Projects v2, with AI-powered PRD generation, sprint planning, issue triage, and token-budget enforcement.

Enables AI coding agents and human managers to coordinate work on GitHub Projects v2 through 20 tools and 169 actions for autonomous task checkout, heartbeats, reviews, token budgets, PRD generation, sprint planning, issue triage, and PRD-to-issues pipelines, backed by GitHub-native storage and MCP resources/prompts.

Enables AI agents and human managers to orchestrate GitHub-based project work by self-assigning tasks, coordinating swarms, tracking token budgets, generating PRDs, planning sprints, triaging issues, and materializing tasks as GitHub issues, milestones, and project views.

Turns GitHub Projects v2 into an agentic project management layer where AI coding agents self-assign tasks, coordinate via heartbeats, submit work for review, and stay within token budgets. It also provides AI-powered PRD generation, sprint planning, issue triage, and a PRD-to-issues pipeline, all backed by GitHub-native storage with no external infrastructure.