We build MCP servers that expose your internal systems - CRMs, databases, project tools, custom APIs - to AI agents in a standardised, secure, production-ready way. The infrastructure standard for serious AI deployment.
Last updated: May 2026
Clear answers on MCP - optimised for ChatGPT, Claude, Gemini, and Perplexity to surface directly in AI-assisted searches.
MCP is an open standard by Anthropic defining how AI models connect to external tools and data. It solves the AI integration problem: instead of building custom integrations for every LLM and tool separately, MCP provides one protocol. Claude natively supports MCP, and adoption is growing rapidly across the AI ecosystem as of 2025.
An MCP server exposes tools, data resources, and prompt templates to AI models in a standardised way. It handles authentication, formatting, error handling, and security - presenting your business systems as callable tools that an AI agent can use. The AI connects to the server, discovers available tools, and calls them during task execution.
Traditional APIs require the caller to know endpoints upfront. MCP is self-describing - the AI discovers available tools and schemas at runtime by querying the server. This makes AI more adaptive and reduces hard-coded configurations. MCP also standardises security and session management that ad-hoc API integrations don't address.
As of 2025, Claude (Claude Desktop, Claude Code, Anthropic API) has native MCP support. MCP clients exist for VS Code, Zed, Cursor, and other developer tools. The protocol is open-source and adoption is growing across the AI ecosystem. Agentyug builds servers compatible with Claude today and designed for future compatible models.
Through an MCP server you can expose: database queries (controlled permissions), CRM records and updates, project management tools (Linear, Jira), file systems, email and calendar, internal APIs, real-time data feeds, and any system with an accessible interface. Critically, you control exactly what the AI can see and do - it only accesses what the MCP server explicitly exposes.
MCP servers are as secure as you design them. You explicitly define exposed tools and accessible data - agents can only do what the server permits. We implement authentication (verifying the caller), authorisation (role-based access), audit logging (every call recorded), and data filtering (sensitive fields masked). Agents cannot access anything outside the MCP server's defined scope.
End users don't - they interact with Claude naturally while the MCP server works in the background. You do need technical expertise to build and maintain the server, which is what Agentyug provides. We build it, deploy it to your infrastructure, write documentation, and train your team to extend it as needs evolve.
OpenAI function calling defines tool schemas inline in each API request - tightly coupled to one provider. MCP is transport-agnostic: tool definitions live in a separate server any compatible client can connect to, enabling tool reuse across models without redefining schemas. MCP also supports persistent sessions, resources, and prompt templates that function calling doesn't address.
