7 posts tagged with "MCP"

Connecting GitHub to AI systems securely and efficiently is a growing challenge for enterprise DevOps teams. The Model Context Protocol provides a standardized approach to connect AI agents with GitHub repositories, issues, pull requests, and workflows—but deploying these connections securely at enterprise scale requires proper infrastructure. This guide shows DevOps engineers how to implement GitHub MCP integrations that meet enterprise security requirements while enabling AI-powered automation across development pipelines.

Comprehensive data analysis of AI platform adoption, security challenges, and infrastructure requirements for enterprise deployments

The enterprise AI landscape demands unprecedented infrastructure planning and governance. With 78% of global companies now using AI in at least one business function, engineering leaders face critical decisions about platform selection, security frameworks, and deployment strategies. Organizations implementing proper AI infrastructure governance through solutions like MintMCP's enterprise gateway achieve measurable advantages in deployment speed, compliance, and operational control.

AI assistants are most useful when they can access internal data and tools via MCP. MCP gateways help to make that process easier by managing connections and authentication for your organization. This article compares LiteLLM's MCP offering as part of their LLM proxy to the MintMCP - a gateway built specifically for enterprises using MCP internally.

Deploying MCP servers in production requires choosing the right approach for your use case. This guide covers the four main deployment patterns and helps you select between Cloudflare Workers, Vercel Functions, and MCP gateways based on your specific requirements.

AI agents need access to tools, data sources, and APIs. The Model Context Protocol (MCP) standardizes these interactions, but managing multiple MCP servers introduces complexity. MCP gateways address the technical problems that emerge in production deployments.

Custom GPTs become powerful when they can interact with external tools and services. The Model Context Protocol (MCP) provides a standardized way for servers to expose tools, resources, and prompts that AI assistants can discover and invoke. By connecting MCP servers to Custom GPTs, you can unlock access to a growing ecosystem of MCP-compatible tools without building custom integrations for each one.

When you first discover the Model Context Protocol (MCP), it can feel a bit like magic: suddenly your AI assistant can read from a database, update a CRM record, or spin up cloud resources - all through a single, standard interface. But as soon as you try to move beyond a demo, you'll run into practical questions: How do you secure these tool calls? Who keeps track of rate limits and audit logs? Where do you plug in observability? That's where an MCP gateway comes in. Think of it as the operations and security layer that makes MCP usable in production - similar to how an API gateway fronts traditional REST or gRPC services.