"A guide to controlling a computer mouse" matching MCP connectors:
Matching Connector Tools:
Give your AI assistant access to real Helm chart data. No more hallucinated values.yaml files.
Deploy a GitHub repo to a live HTTPS URL from your AI tool; read logs, set variables, resize apps.
Publish the website you built with AI to a live public URL — straight from chat, no setup.
JWT-gated LLM gateway: authenticate (bcrypt/JWT), then run a LangChain-on-Vertex Gemini completion.
Google-OAuth-gated LLM gateway: verify a Google ID token, then run a Gemini (Vertex AI) completion f
Deploy files, sites, and Dockerfile apps to live URLs + private drives for agent memory.
Compare LLM inference costs vs OpenAI/Anthropic/DeepSeek. Gonka is up to 6800x cheaper.
Provision, SSH into, run commands on, and manage Linux VPSes from an AI agent. Pay USDC over x402 (Base) or by card over HTTP 402, a running box in under 60s. No signup, no API key to buy. This remote endpoint offers free browse/discovery, quotes, and server status.
TitanStore provides AI agents with programmatic access to compute credits, API keys, cloud storage, and GPU capacity. Search products, manage cart, and complete purchases in a single agentic workflow. No authentication required.
B2B MCP server that lets business operators launch and operate complete branded taxi / food delivery / LP gas / on-demand apps from a single conversation. Multi-tenant, OAuth 2.1, 87+ active operators across 14 LATAM countries.
Zero-Ops deploy of a private AI workspace to your own VPS — from your AI chat. Free and open-source.
Provides read access to your GKE and Kubernetes resources.
Provides capabilities that let LLM agents perform a range of infrastructure management tasks.
Provides tools to manage Memorystore for Valkey instances and backups.
AI first app deployment, unlike lovable or figma make, webslop.ai lets you or your ai of choice setup node.js apps or static sites in seconds. Designed be be the perfect place for you to deploy websites and apps super fast to the rest of the world and has a generous free tier.
The AWS Knowledge MCP server is a fully managed remote Model Context Protocol server that provides real-time access to official AWS content in an LLM-compatible format. It offers structured access to AWS documentation, code samples, blog posts, What's New announcements, Well-Architected best practices, and regional availability information for AWS APIs and CloudFormation resources. Key capabilities include searching and reading documentation in markdown format, getting content recommendations, listing AWS regions, and checking regional availability for services and features.
The Telnyx MCP server is an official implementation of the Model Context Protocol that enables AI clients (like Claude Desktop, Cursor, and OpenAI Agents) to interact with Telnyx's telephony, messaging, and AI assistant APIs. It provides comprehensive capabilities including making and managing phone calls, sending SMS/MMS messages, purchasing and configuring phone numbers, creating AI assistants with custom instructions, managing cloud storage buckets, scraping and embedding website content, and handling integration secrets. The server exists as both a local implementation and a remotely hosted version, allowing developers to integrate real-world communication infrastructure directly into AI applications.
The BigQuery remote MCP server is a fully managed service that uses the Model Context Protocol to connect AI applications and LLMs to BigQuery data sources. It provides secure, standardized tools for AI agents to list datasets and tables, retrieve schemas, generate and execute SQL queries through natural language, and analyze data—enabling direct access to enterprise analytics data without requiring manual SQL coding.
The Google GKE MCP server is a managed Model Context Protocol server that provides AI applications with tools to manage Google Kubernetes Engine (GKE) clusters and Kubernetes resources. It exposes a structured, discoverable interface that allows AI agents to interact with GKE and Kubernetes APIs, enabling them to inspect cluster configurations, retrieve Kubernetes resource YAMLs, monitor operations like cluster upgrades, diagnose issues, and optimize costs—all without needing to parse text output or use complex kubectl commands.