"Startups in AI, Large Language Models, and AI Agents" matching MCP servers:

Enables MCP-compatible clients to search approved local Markdown runbooks, retrieve selected local host health details, and get bounded Kubernetes pod-status summaries for allowlisted clusters and namespaces.

A Model Context Protocol (MCP) server that exposes the EasyDeploy public REST API as tools for Claude, Cursor, Claude Code, and other MCP clients.

MCP server that lets an AI agent manage Proxmox VE in natural language, with policy-driven security, read-only mode, two-step confirmations, and audit logging.

Intelligent AI routing and integration platform for seamless provider switching

Enables AI-powered image and video analysis using Google Gemini and Vertex AI models. Supports analyzing single or multiple images, detecting objects with bounding boxes, and video content analysis through natural language prompts.

Integrates Claude Desktop with Azure AI Search, allowing users to query search indexes using keyword, vector, or hybrid search methods.

Enables AI agents to access Cloudflare Workers AI for serverless LLM chat completions, text embeddings, and image generation via MCP tools.

An MCP server that enables running commands against a ServiceNow instance via its REST APIs, including Table, Aggregate, Attachment, Import Set, Batch, CMDB, Service Catalog, Change Management, and Knowledge APIs.

Implementation of Model Context Protocol (MCP) server that provides tools for accessing Google Cloud's Vertex AI Gemini models, supporting features like web search grounding and direct knowledge answering for coding assistance and general queries.

Deterministic AI data-center design tools for sizing, validation, and conceptual rack/site layouts. Supports NVIDIA Hopper, Blackwell, and Rubin profiles. Calculations require a registered AIDC API key; results preserve warnings, RFIs, and pending validations.

Provisions Google Analytics 4 for local AI/web projects by creating properties and web data streams, injecting gtag snippets, and saving configuration.

Enables users to describe their LLM fine-tuning job once and get the cheapest, fastest, and most balanced GPU options across a dozen cloud providers in seconds.

Enables AI assistants to manage Amazon SageMaker AI resources including endpoints, jobs, pipelines, MLflow tracking servers, domains, models, model cards, and apps.

Enables comprehensive integration with Azure AI services including OpenAI, Cognitive Services, Computer Vision, and Face API through a mission-critical MCP server. Provides enterprise-grade reliability with high availability, observability, chaos engineering, and secure multi-region deployment capabilities.

Enables AI agents to discover and invoke backend tools over MCP JSON-RPC while enforcing 3-legged OAuth 2.0 identity propagation, role-based access control, and protocol transcoding to REST APIs.

Exposes SAP AI Core APIs as MCP tools, enabling AI assistants to manage AI Core lifecycle and administration through natural language.

Enables AI assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.

Enables interaction with Azure AI Agent Service by automatically discovering agents and creating MCP tools for querying and managing agent workflows.

Enables interaction with Azure AI Foundry services for model exploration, deployment, and performance evaluation. It provides tools for managing knowledge bases via AI Search Service, executing fine-tuning jobs, and orchestrating AI agents through natural language.

Enables AI assistants to generate images using Cloudflare Workers AI models directly through the Model Context Protocol. It supports various models like Flux and Stable Diffusion, providing seamless image generation and storage via Cloudflare R2.